notebook11-Binning data for Tables

Load data

Change factor levels to N, L M H E, etc

str(data)

'data.frame':   3180108 obs. of  51 variables:
 $ Date                 : Factor w/ 29 levels "20191118","20191119",..: 1 2 2 2 2 2 2 2 2 2 ...
 $ File.name            : Factor w/ 125 levels "20191118_view1_",..: 1 2 2 2 2 2 2 2 2 2 ...
 $ X                    : num  0.1632 0.0384 0.0206 0.0206 0.0204 ...
 $ Y                    : num  0.0405 0.3385 0.3315 0.334 0.3339 ...
 $ Z                    : num  -0.0774 -0.0235 -0.0245 -0.0219 -0.0221 ...
 $ Track                : Factor w/ 8 levels "1","2","3","4",..: 1 3 3 3 3 3 3 3 3 3 ...
 $ View                 : Factor w/ 22 levels "_1","1 ","1_",..: 3 3 3 3 3 3 3 3 3 3 ...
 $ D_V_T                : Factor w/ 538 levels "20191118_1__1",..: 1 4 4 4 4 4 4 4 4 4 ...
 $ D_V                  : Factor w/ 125 levels "20191118_1_",..: 1 2 2 2 2 2 2 2 2 2 ...
 $ Flow.rate            : Factor w/ 5 levels "0","0.6","3",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Chlorophyll          : Factor w/ 5 levels "No Chlorophyll",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Guano                : Factor w/ 2 levels "Absent","Present": 1 1 1 1 1 1 1 1 1 1 ...
 $ Light                : Factor w/ 2 levels "Absent","Present": 2 2 2 2 2 2 2 2 2 2 ...
 $ Flume                : Factor w/ 1 level "Horizontal": 1 1 1 1 1 1 1 1 1 1 ...
 $ Flow.Direction       : Factor w/ 1 level "Horizontal": 1 1 1 1 1 1 1 1 1 1 ...
 $ dx                   : num  0.00283 -0.017826 0.000035 -0.000199 0.002657 ...
 $ dy                   : num  -0.00183 -0.007 0.00252 -0.00016 0.0004 ...
 $ dz                   : num  0.00056 -0.001051 0.002653 -0.000194 -0.002498 ...
 $ d                    : num  0.003415 0.01918 0.003659 0.000321 0.003669 ...
 $ vx                   : num  0.0849 -0.53478 0.00105 -0.00597 0.07971 ...
 $ vy                   : num  -0.0548 -0.21 0.0756 -0.0048 0.012 ...
 $ vz                   : num  0.0168 -0.03153 0.07959 -0.00582 -0.07494 ...
 $ v                    : num  0.10246 0.5754 0.10978 0.00962 0.11006 ...
 $ heading              : num  2.1443 -1.945 0.0139 -2.248 1.4214 ...
 $ pitch                : num  0.1647 -0.0548 0.8111 -0.6497 -0.749 ...
 $ turn.anglexy         : num  1.3273 0.1129 0.0619 0.0616 0.061 ...
 $ turn.angleyz         : num  2.66 1.64 1.64 1.64 1.64 ...
 $ turn.angle           : num  31.1 92.1 107.5 142 95.3 ...
 $ vel.turn.angle       : num  0 125.7 107.5 142 95.3 ...
 $ vel                  : num  0 0.3389 0.3426 0.0597 0.0598 ...
 $ vel.flow             : num  -0.0548 -0.21 0.0756 -0.0048 0.012 ...
 $ trim.X               : num  0.1632 0.0384 0.0206 0.0206 0.0204 ...
 $ xsmooth              : num  0.074 0.0384 0.0206 0.023 0.0367 ...
  ..- attr(*, "k")= int 31
 $ ysmooth              : num  0.327 0.332 0.334 0.334 0.334 ...
  ..- attr(*, "k")= int 31
 $ zsmooth              : num  -0.0266 -0.0245 -0.0235 -0.0235 -0.0232 ...
  ..- attr(*, "k")= int 31
 $ smooth.dx            : num  -0.03558 -0.01779 0.00246 0.01366 -0.01334 ...
 $ smooth.dy            : num  0.00472 0.00236 0.00016 0.00024 0 ...
 $ smooth.dz            : num  0.002102 0.001051 0 0.000312 0 ...
 $ smooth.d             : num  0.03596 0.01798 0.00246 0.01367 0.01334 ...
 $ smooth.vx            : num  -1.0675 -0.5337 0.0737 0.4098 -0.4001 ...
 $ smooth.vy            : num  0.1416 0.0708 0.0048 0.0072 0 ...
 $ smooth.vz            : num  0.06306 0.03153 0 0.00936 0 ...
 $ smooth.v             : num  1.0787 0.5393 0.0739 0.41 0.4001 ...
 $ smooth.heading       : num  -82.4 -82.4 86.3 89 -90 ...
 $ heading.pi           : num  -82.4 -82.4 86.3 89 -90 ...
 $ smooth.pitch         : num  3.35 3.35 0 1.31 0 ...
 $ pitch.perfect        : num  3.35 3.35 0 1.31 0 ...
 $ turn.anglexysmooth   : num  0.2226 0.1153 0.0616 0.0689 0.1094 ...
  ..- attr(*, "k")= int 31
 $ turn.angleyzsmooth   : num  1.65 1.64 1.64 1.64 1.64 ...
  ..- attr(*, "k")= int 31
 $ turn.angle.smooth    : num  NA NA NA NA NA NA NA NA NA NA ...
 $ vel.turn.angle.smooth: num  0 NaN 168.24 3.02 178.35 ...
 - attr(*, "na.action")= 'omit' Named int [1:3401545] 1 2 3 4 5 6 7 8 9 10 ...
  ..- attr(*, "names")= chr [1:3401545] "1" "2" "3" "4" ...

aggregate data by factor levels Find max, min, mean and sd for v and vel.flow

```r

agg.data <- aggregate(data, by = list(data$Light, data$Flow.rate, data$Chlorophyll, data$Guano), FUN = mean)

<!-- rnb-source-end -->

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Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA Warning in mean.default(X[[i]], …) : argument is not numeric or logical: returning NA

<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuaGVhZChhZ2cuZGF0YSlcbmBgYFxuYGBgclxuYWdnLmRhdGEgPC0gYWdnLmRhdGFbIC1jKDU6NiwgMTA6MTcpIF1cbmhlYWQoYWdnLmRhdGEpXG5gYGBcbmBgYCJ9 -->

```r
```r
head(agg.data)

agg.data <- agg.data[ -c(5:6, 10:17) ]
head(agg.data)

<!-- rnb-source-end -->

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```r
```r
colnames(agg.data) <- c(\Light\, \Flow.rate\, \Chlorophyll\, \Guano\, \X\, \Y\, \Z\, \dx\, \dy\, \dz\, \d\, \vx\, \vy\, \vz\, \v\, \heading\, \pitch\, \vel.turn.angle\, \vel.flow\, \trim.X\, \xsmooth\, \ysmooth\, \zsmooth\, \smooth.dx\, \smooth.dy\, \smooth.dz\, \smooth.d\, \smooth.vx\, \smooth.vy\, \smooth.vz\, \smooth.v\, \smooth.heading\, \heading.pi\, \smooth.pitch\, \pitch.perfect\, \turn.anglexysmooth\,  \turn.angleyzsmooth\, \turn.angle.smooth\, \vel.turn.angle.smooth\, \turn.anglexy\, \turn.angleyz\, \turn.angle\)
head(agg.data)

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```r
```r
## calculating variance of the vector from circular data
var(circ)

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<!-- rnb-output-begin eyJkYXRhIjoiWzFdIE5BXG4ifQ== -->

[1] NA

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<!-- rnb-text-begin -->


Sub-setting factors into separate data sets to calculate mean vectors and dispersion for angle data

<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxubGlicmFyeShjaXJjdWxhcilcblxuYGBgXG5gYGAifQ== -->

```r
```r
library(circular)

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiUGFja2FnZSAnY2lyY3VsYXInLCAwLjQtOTMgKDIwMTctMDYtMjYpLiBUeXBlICdoZWxwKENpcmN1bGFyKScgZm9yIHN1bW1hcnkgaW5mb3JtYXRpb24uIFxuIFBsZWFzZSByZXBvcnQgYW55IGJ1ZyBvciBjb21tZW50cyB0byBDbGF1ZGlvIEFnb3N0aW5lbGxpIDxjbGF1ZGlvLmFnb3N0aW5lbGxpQHVuaXRuLml0PlxuXG5BdHRhY2hpbmcgcGFja2FnZTogw6LigqzLnGNpcmN1bGFyw6LigqzihKJcblxuVGhlIGZvbGxvd2luZyBvYmplY3RzIGFyZSBtYXNrZWQgZnJvbSDDouKCrMuccGFja2FnZTpzdGF0c8Oi4oKs4oSiOlxuXG4gICAgc2QsIHZhclxuIn0= -->

Package ‘circular’, 0.4-93 (2017-06-26). Type ‘help(Circular)’ for summary information. Please report any bug or comments to Claudio Agostinelli claudio.agostinelli@unitn.it

Attaching package: ‘circular’

The following objects are masked from ‘package:stats’:

sd, var

<!-- rnb-output-end -->

<!-- rnb-source-begin {"data":"```r\n```r\n#####################################################################\n\n   ##  Light On, NO Flow\n\n####################################################################\n\nLNN<-data[data$Light==\\Present\\,]\nLNN <- LNN[LNN$Flow.rate==\\No Flow\\,]\nLNN <- LNN[LNN$Chlorophyll==\\No Chlorophyll\\,]\nLNN <- LNN[LNN$Guano==\\Absent\\,]\n\nLNL<-data[data$Light==\\Present\\,]\nLNL <- LNL[LNL$Flow.rate==\\No Flow\\,]\nLNL <- LNL[LNL$Chlorophyll==\\Low Chlorophyll\\,]\nLNL <- LNL[LNL$Guano==\\Absent\\,]\n\nLNM<-data[data$Light==\\Present\\,]\nLNM <- LNM[LNM$Flow.rate==\\No Flow\\,]\nLNM <- LNM[LNM$Chlorophyll==\\Medium Chlorophyll\\,]\nLNM <- LNM[LNM$Guano==\\Absent\\,]\n\nLNH<-data[data$Light==\\Present\\,]\nLNH <- LNH[LNH$Flow.rate==\\No Flow\\,]\nLNH <- LNH[LNH$Chlorophyll==\\High Chlorophyll\\,]\nLNH <- LNH[LNH$Guano==\\Absent\\,]\n\nLNG<-data[data$Light==\\Present\\,]\nLNG <- LNG[LNG$Flow.rate==\\No Flow\\,]\nLNG <- LNG[LNG$Chlorophyll==\\No Chlorophyll\\,]\nLNG <- LNG[LNG$Guano==\\Present\\,]\n##############################################################\n\n##    Lights On, Low Flow\n\n###############################################################\n\nLLN<-data[data$Light==\\Present\\,]\nLLN <- LLN[LLN$Flow.rate==\\Low Flow\\,]\nLLN <- LLN[LLN$Chlorophyll==\\No Chlorophyll\\,]\nLLN <- LLN[LLN$Guano==\\Absent\\,]\n\nLLL<-data[data$Light==\\Present\\,]\nLLL <- LLL[LLL$Flow.rate==\\Low Flow\\,]\nLLL <- LLL[LLL$Chlorophyll==\\Low Chlorophyll\\,]\nLLL <- LLL[LLL$Guano==\\Absent\\,]\n\nLLM<-data[data$Light==\\Present\\,]\nLLM <- LLM[LLM$Flow.rate==\\Low Flow\\,]\nLLM <- LLM[LLM$Chlorophyll==\\Medium Chlorophyll\\,]\nLLM <- LLM[LLM$Guano==\\Absent\\,]\n\nLLH<-data[data$Light==\\Present\\,]\nLLH <- LLH[LLH$Flow.rate==\\Low Flow\\,]\nLLH <- LLH[LLH$Chlorophyll==\\High Chlorophyll\\,]\nLLH <- LLH[LLH$Guano==\\Absent\\,]\n\nLLG<-data[data$Light==\\Present\\,]\nLLG <- LLG[LLG$Flow.rate==\\Low Flow\\,]\nLLG <- LLG[LLG$Chlorophyll==\\No Chlorophyll\\,]\nLLG <- LLG[LLG$Guano==\\Present\\,]\n#####################################################################\n\n##    Light On, Medium Flow\n\n######################################################################\n\nLMN<-data[data$Light==\\Present\\,]\nLMN <- LMN[LMN$Flow.rate==\\Medium Flow\\,]\nLMN <- LMN[LMN$Chlorophyll==\\No Chlorophyll\\,]\nLMN <- LMN[LMN$Guano==\\Absent\\,]\n\nLML<-data[data$Light==\\Present\\,]\nLML <- LML[LML$Flow.rate==\\Medium Flow\\,]\nLML <- LML[LML$Chlorophyll==\\Low Chlorophyll\\,]\nLML <- LML[LML$Guano==\\Absent\\,]\n\nLMM<-data[data$Light==\\Present\\,]\nLMM <- LMM[LMM$Flow.rate==\\Medium Flow\\,]\nLMM <- LMM[LMM$Chlorophyll==\\Medium Chlorophyll\\,]\nLMM <- LMM[LMM$Guano==\\Absent\\,]\n\nLMH<-data[data$Light==\\Present\\,]\nLMH <- LMH[LMH$Flow.rate==\\Medium Flow\\,]\nLMH <- LMH[LMH$Chlorophyll==\\High Chlorophyll\\,]\nLMH <- LMH[LMH$Guano==\\Absent\\,]\n\nLMG<-data[data$Light==\\Present\\,]\nLMG <- LMG[LMG$Flow.rate==\\Medium Flow\\,]\nLMG <- LMG[LMG$Chlorophyll==\\No Chlorophyll\\,]\nLMG <- LMG[LMG$Guano==\\Present\\,]\n################################################################\n\n###    Light On, High Flow\n\n####################################################################\n\nLHN<-data[data$Light==\\Present\\,]\nLHN <- LHN[LHN$Flow.rate==\\High Flow\\,]\nLHN <- LHN[LHN$Chlorophyll==\\No Chlorophyll\\,]\nLHN <- LHN[LHN$Guano==\\Absent\\,]\n\nLHL<-data[data$Light==\\Present\\,]\nLHL <- LHL[LHL$Flow.rate==\\High Flow\\,]\nLHL <- LHL[LHL$Chlorophyll==\\Low Chlorophyll\\,]\nLHL <- LHL[LHL$Guano==\\Absent\\,]\n\nLHM<-data[data$Light==\\Present\\,]\nLHM <- LHM[LHM$Flow.rate==\\High Flow\\,]\nLHM <- LHM[LHM$Chlorophyll==\\Medium Chlorophyll\\,]\nLHM <- LHM[LHM$Guano==\\Absent\\,]\n\nLHH<-data[data$Light==\\Present\\,]\nLHH <- LHH[LHH$Flow.rate==\\High Flow\\,]\nLHH <- LHH[LHH$Chlorophyll==\\High Chlorophyll\\,]\nLHH <- LHH[LHH$Guano==\\Absent\\,]\n\nLHG<-data[data$Light==\\Present\\,]\nLHG <- LHG[LHG$Flow.rate==\\High Flow\\,]\nLHG <- LHG[LHG$Chlorophyll==\\No Chlorophyll\\,]\nLHG <- LHG[LHG$Guano==\\Present\\,]\n################################################################\n\n\n###    Light On, Extreme Flow\n\n####################################################################\n\nLEN<-data[data$Light==\\Present\\,]\nLEN <- LEN[LEN$Flow.rate==\\Extreme Flow\\,]\nLEN <- LEN[LEN$Chlorophyll==\\No Chlorophyll\\,]\nLEN <- LEN[LEN$Guano==\\Absent\\,]\n\nLEL<-data[data$Light==\\Present\\,]\nLEL <- LEL[LEL$Flow.rate==\\Extreme Flow\\,]\nLEL <- LEL[LEL$Chlorophyll==\\Low Chlorophyll\\,]\nLEL <- LEL[LEL$Guano==\\Absent\\,]\n\nLEM<-data[data$Light==\\Present\\,]\nLEM <- LEM[LEM$Flow.rate==\\Extreme Flow\\,]\nLEM <- LEM[LEM$Chlorophyll==\\Medium Chlorophyll\\,]\nLEM <- LEM[LEM$Guano==\\Absent\\,]\n\nLEH<-data[data$Light==\\Present\\,]\nLEH <- LEH[LEH$Flow.rate==\\Extreme Flow\\,]\nLEH <- LEH[LEH$Chlorophyll==\\High Chlorophyll\\,]\nLEH <- LEH[LEH$Guano==\\Absent\\,]\n\nLEG<-data[data$Light==\\Present\\,]\nLEG <- LEG[LEG$Flow.rate==\\Extreme Flow\\,]\nLEG <- LEG[LEG$Chlorophyll==\\No Chlorophyll\\,]\nLEG <- LEG[LEG$Guano==\\Present\\,]\n################################################################\n\n#####################################################################\n\n   ##  Light Off, NO Flow\n\n####################################################################\n\nDNN<-data[data$Light==\\Absent\\,]\nDNN <- DNN[DNN$Flow.rate==\\No Flow\\,]\nDNN <- DNN[DNN$Chlorophyll==\\No Chlorophyll\\,]\nDNN <- DNN[DNN$Guano==\\Absent\\,]\n\nDNL<-data[data$Light==\\Absent\\,]\nDNL <- DNL[DNL$Flow.rate==\\No Flow\\,]\nDNL <- DNL[DNL$Chlorophyll==\\Low Chlorophyll\\,]\nDNL <- DNL[DNL$Guano==\\Absent\\,]\n\nDNM<-data[data$Light==\\Absent\\,]\nDNM <- DNM[DNM$Flow.rate==\\No Flow\\,]\nDNM <- DNM[DNM$Chlorophyll==\\Medium Chlorophyll\\,]\nDNM <- DNM[DNM$Guano==\\Absent\\,]\n\nDNH <-data[data$Light==\\Absent\\,]\nDNH <- DNH[DNH$Flow.rate==\\No Flow\\,]\nDNH <- DNH[DNH$Chlorophyll==\\High Chlorophyll\\,]\nDNH <- DNH[DNH$Guano==\\Absent\\,]\n\nDNG<-data[data$Light==\\Absent\\,]\nDNG <- DNG[DNG$Flow.rate==\\No Flow\\,]\nDNG <- DNG[DNG$Chlorophyll==\\No Chlorophyll\\,]\nDNG <- DNG[DNG$Guano==\\Present\\,]\n##############################################################\n\n##    Lights Off, Low Flow\n\n###############################################################\n\nDLN <-data[data$Light==\\Absent\\,]\nDLN <- DLN[DLN$Flow.rate==\\Low Flow\\,]\nDLN <- DLN[DLN$Chlorophyll==\\No Chlorophyll\\,]\nDLN <- DLN[DLN$Guano==\\Absent\\,]\n\nDLL <-data[data$Light==\\Absent\\,]\nDLL <- DLL[DLL$Flow.rate==\\Low Flow\\,]\nDLL <- DLL[DLL$Chlorophyll==\\Low Chlorophyll\\,]\nDLL <- DLL[DLL$Guano==\\Absent\\,]\n\nDLM <-data[data$Light==\\Absent\\,]\nDLM <- DLM[DLM$Flow.rate==\\Low Flow\\,]\nDLM <- DLM[DLM$Chlorophyll==\\Medium Chlorophyll\\,]\nDLM <- DLM[DLM$Guano==\\Absent\\,]\n\nDLH <-data[data$Light==\\Absent\\,]\nDLH <- DLH[DLH$Flow.rate==\\Low Flow\\,]\nDLH <- DLH[DLH$Chlorophyll==\\High Chlorophyll\\,]\nDLH <- DLH[DLH$Guano==\\Absent\\,]\n\nDLG <-data[data$Light==\\Absent\\,]\nDLG <- DLG[DLG$Flow.rate==\\Low Flow\\,]\nDLG <- DLG[DLG$Chlorophyll==\\No Chlorophyll\\,]\nDLG <- DLG[DLG$Guano==\\Present\\,]\n#####################################################################\n\n##    Light Off, Medium Flow\n\n######################################################################\n\nDMN <-data[data$Light==\\Absent\\,]\nDMN <- DMN[DMN$Flow.rate==\\Medium Flow\\,]\nDMN <- DMN[DMN$Chlorophyll==\\No Chlorophyll\\,]\nDMN <- DMN[DMN$Guano==\\Absent\\,]\n\nDML <-data[data$Light==\\Absent\\,]\nDML <- DML[DML$Flow.rate==\\Medium Flow\\,]\nDML <- DML[DML$Chlorophyll==\\Low Chlorophyll\\,]\nDML <- DML[DML$Guano==\\Absent\\,]\n\nDMM <-data[data$Light==\\Absent\\,]\nDMM <- DMM[DMM$Flow.rate==\\Medium Flow\\,]\nDMM <- DMM[DMM$Chlorophyll==\\Medium Chlorophyll\\,]\nDMM <- DMM[DMM$Guano==\\Absent\\,]\n\nDMH <-data[data$Light==\\Absent\\,]\nDMH <- DMH[DMH$Flow.rate==\\Medium Flow\\,]\nDMH <- DMH[DMH$Chlorophyll==\\High Chlorophyll\\,]\nDMH <- DMH[DMH$Guano==\\Absent\\,]\n\nDMG <-data[data$Light==\\Absent\\,]\nDMG <- DMG[DMG$Flow.rate==\\Medium Flow\\,]\nDMG <- DMG[DMG$Chlorophyll==\\No Chlorophyll\\,]\nDMG <- DMG[DMG$Guano==\\Present\\,]\n################################################################\n\n###    Light Off, High Flow\n\n####################################################################\n\nDHN<-data[data$Light==\\Absent\\,]\nDHN <- DHN[DHN$Flow.rate==\\High Flow\\,]\nDHN <- DHN[DHN$Chlorophyll==\\No Chlorophyll\\,]\nDHN <- DHN[DHN$Guano==\\Absent\\,]\n\nDHL<-data[data$Light==\\Absent\\,]\nDHL <- DHL[DHL$Flow.rate==\\High Flow\\,]\nDHL <- DHL[DHL$Chlorophyll==\\Low Chlorophyll\\,]\nDHL <- DHL[DHL$Guano==\\Absent\\,]\n\nDHM<-data[data$Light==\\Absent\\,]\nDHM <- DHM[DHM$Flow.rate==\\High Flow\\,]\nDHM <- DHM[DHM$Chlorophyll==\\Medium Chlorophyll\\,]\nDHM <- DHM[DHM$Guano==\\Absent\\,]\n\nDHH<-data[data$Light==\\Absent\\,]\nDHH <- DHH[DHH$Flow.rate==\\High Flow\\,]\nDHH <- DHH[DHH$Chlorophyll==\\High Chlorophyll\\,]\nDHH <- DHH[DHH$Guano==\\Absent\\,]\n\nDHG<-data[data$Light==\\Absent\\,]\nDHG <- DHG[DHG$Flow.rate==\\High Flow\\,]\nDHG <- DHG[DHG$Chlorophyll==\\No Chlorophyll\\,]\nDHG <- DHG[DHG$Guano==\\Present\\,]\n################################################################\n\n\n###    Light Off, Extreme Flow\n\n####################################################################\n\nDEN<-data[data$Light==\\Absent\\,]\nDEN <- DEN[DEN$Flow.rate==\\Extreme Flow\\,]\nDEN <- DEN[DEN$Chlorophyll==\\No Chlorophyll\\,]\nDEN <- DEN[DEN$Guano==\\Absent\\,]\n\nDEL<-data[data$Light==\\Absent\\,]\nDEL <- DEL[DEL$Flow.rate==\\Extreme Flow\\,]\nDEL <- DEL[DEL$Chlorophyll==\\Low Chlorophyll\\,]\nDEL <- DEL[DEL$Guano==\\Absent\\,]\n\nDEM<-data[data$Light==\\Absent\\,]\nDEM <- DEM[DEM$Flow.rate==\\Extreme Flow\\,]\nDEM <- DEM[DEM$Chlorophyll==\\Medium Chlorophyll\\,]\nDEM <- DEM[DEM$Guano==\\Absent\\,]\n\nDEH<-data[data$Light==\\Absent\\,]\nDEH <- DEH[DEH$Flow.rate==\\Extreme Flow\\,]\nDEH <- DEH[DEH$Chlorophyll==\\High Chlorophyll\\,]\nDEH <- DEH[DEH$Guano==\\Absent\\,]\n\nDEG<-data[data$Light==\\Absent\\,]\nDEG <- DEG[DEG$Flow.rate==\\Extreme Flow\\,]\nDEG <- DEG[DEG$Chlorophyll==\\No Chlorophyll\\,]\nDEG <- DEG[DEG$Guano==\\Present\\,]\n################################################################\n  ### setting data as circular\n##   Lights on\n\nLNN <- circular(LNN$turn.angle, units=\\degrees\\, template=\\geographics\\) #assign LNN subset to \\LNN\\ variable\nLNL <- circular(LNL$turn.angle, units=\\degrees\\, template=\\geographics\\) #LNL\nLNM <- circular(LNM$turn.angle, units=\\degrees\\, template=\\geographics\\) #LNM\nLNH <- circular(LNH$turn.angle, units=\\degrees\\, template=\\geographics\\) #LNH\nLNG <- circular(LNG$turn.angle, units=\\degrees\\, template=\\geographics\\) #LNG\n\nLLN <- circular(LLN$turn.angle, units=\\degrees\\, template=\\geographics\\) #LLN\nLLL <- circular(LLL$turn.angle, units=\\degrees\\, template=\\geographics\\) #LLL\nLLM <- circular(LLM$turn.angle, units=\\degrees\\, template=\\geographics\\) #LLM\nLLH <- circular(LLH$turn.angle, units=\\degrees\\, template=\\geographics\\) #LLH\nLLG <- circular(LLG$turn.angle, units=\\degrees\\, template=\\geographics\\) #LLG\n\nLMN <- circular(LMN$turn.angle, units=\\degrees\\, template=\\geographics\\) #LMN\nLML <- circular(LML$turn.angle, units=\\degrees\\, template=\\geographics\\) #LML\nLMM <- circular(LMM$turn.angle, units=\\degrees\\, template=\\geographics\\) #LMM\nLMH <- circular(LMH$turn.angle, units=\\degrees\\, template=\\geographics\\) #LMH\nLMG <- circular(LMG$turn.angle, units=\\degrees\\, template=\\geographics\\) #LMG\n\nLHN <- circular(LHN$turn.angle, units=\\degrees\\, template=\\geographics\\) #LHN\nLHL <- circular(LHL$turn.angle, units=\\degrees\\, template=\\geographics\\) #LHL\nLHM <- circular(LHM$turn.angle, units=\\degrees\\, template=\\geographics\\) #LHM\nLHH <- circular(LHH$turn.angle, units=\\degrees\\, template=\\geographics\\) #LHH\nLHG <- circular(LHG$turn.angle, units=\\degrees\\, template=\\geographics\\) #LHG\n\nLEN <- circular(LEN$turn.angle, units=\\degrees\\, template=\\geographics\\) #LEN\nLEL <- circular(LEL$turn.angle, units=\\degrees\\, template=\\geographics\\) #LEL\nLEM <- circular(LEM$turn.angle, units=\\degrees\\, template=\\geographics\\) #LEM\nLEH <- circular(LEH$turn.angle, units=\\degrees\\, template=\\geographics\\) #LEH\nLEG <- circular(LEG$turn.angle, units=\\degrees\\, template=\\geographics\\) #LEG\n\n############  Lights Off\n\nDNN <- circular(DNN$turn.angle, units=\\degrees\\, template=\\geographics\\) #DNN\nDNL <- circular(DNL$turn.angle, units=\\degrees\\, template=\\geographics\\) #DNL\nDNM <- circular(DNM$turn.angle, units=\\degrees\\, template=\\geographics\\) #DNM\nDNH <- circular(DNH$turn.angle, units=\\degrees\\, template=\\geographics\\) #DNH\nDNG <- circular(DNG$turn.angle, units=\\degrees\\, template=\\geographics\\) #DNG\n\nDLN <- circular(DLN$turn.angle, units=\\degrees\\, template=\\geographics\\) #DLN\nDLL <- circular(DLL$turn.angle, units=\\degrees\\, template=\\geographics\\) #DLL\nDLM <- circular(DLM$turn.angle, units=\\degrees\\, template=\\geographics\\) #DLM\nDLH <- circular(DLH$turn.angle, units=\\degrees\\, template=\\geographics\\) #DLH\nDLG <- circular(DLG$turn.angle, units=\\degrees\\, template=\\geographics\\) #DLG\n\nDMN <- circular(DMN$turn.angle, units=\\degrees\\, template=\\geographics\\) #DMN\nDML <- circular(DML$turn.angle, units=\\degrees\\, template=\\geographics\\) #DML\nDMM <- circular(DMM$turn.angle, units=\\degrees\\, template=\\geographics\\) #DMM\nDMH <- circular(DMH$turn.angle, units=\\degrees\\, template=\\geographics\\) #DMH\nDMG <- circular(DMG$turn.angle, units=\\degrees\\, template=\\geographics\\) #DMG\n\nDHN <- circular(DHN$turn.angle, units=\\degrees\\, template=\\geographics\\) #DHN\nDHL <- circular(DHL$turn.angle, units=\\degrees\\, template=\\geographics\\) #DHL\nDHM <- circular(DHM$turn.angle, units=\\degrees\\, template=\\geographics\\) #DHM\nDHH <- circular(DHH$turn.angle, units=\\degrees\\, template=\\geographics\\) #DHH\nDHG <- circular(DHG$turn.angle, units=\\degrees\\, template=\\geographics\\) #DHG\n\nDEN <- circular(DEN$turn.angle, units=\\degrees\\, template=\\geographics\\) #DEN\nDEL <- circular(DEL$turn.angle, units=\\degrees\\, template=\\geographics\\) #DEL\nDEM <- circular(DEM$turn.angle, units=\\degrees\\, template=\\geographics\\) #DEM\nDEH <- circular(DEH$turn.angle, units=\\degrees\\, template=\\geographics\\) #DEH\nDEG <- circular(DEG$turn.angle, units=\\degrees\\, template=\\geographics\\) #DEG\n```\n```"} -->

```r
```r
#####################################################################

   ##  Light On, NO Flow

####################################################################

LNN<-data[data$Light==\Present\,]
LNN <- LNN[LNN$Flow.rate==\No Flow\,]
LNN <- LNN[LNN$Chlorophyll==\No Chlorophyll\,]
LNN <- LNN[LNN$Guano==\Absent\,]

LNL<-data[data$Light==\Present\,]
LNL <- LNL[LNL$Flow.rate==\No Flow\,]
LNL <- LNL[LNL$Chlorophyll==\Low Chlorophyll\,]
LNL <- LNL[LNL$Guano==\Absent\,]

LNM<-data[data$Light==\Present\,]
LNM <- LNM[LNM$Flow.rate==\No Flow\,]
LNM <- LNM[LNM$Chlorophyll==\Medium Chlorophyll\,]
LNM <- LNM[LNM$Guano==\Absent\,]

LNH<-data[data$Light==\Present\,]
LNH <- LNH[LNH$Flow.rate==\No Flow\,]
LNH <- LNH[LNH$Chlorophyll==\High Chlorophyll\,]
LNH <- LNH[LNH$Guano==\Absent\,]

LNG<-data[data$Light==\Present\,]
LNG <- LNG[LNG$Flow.rate==\No Flow\,]
LNG <- LNG[LNG$Chlorophyll==\No Chlorophyll\,]
LNG <- LNG[LNG$Guano==\Present\,]
##############################################################

##    Lights On, Low Flow

###############################################################

LLN<-data[data$Light==\Present\,]
LLN <- LLN[LLN$Flow.rate==\Low Flow\,]
LLN <- LLN[LLN$Chlorophyll==\No Chlorophyll\,]
LLN <- LLN[LLN$Guano==\Absent\,]

LLL<-data[data$Light==\Present\,]
LLL <- LLL[LLL$Flow.rate==\Low Flow\,]
LLL <- LLL[LLL$Chlorophyll==\Low Chlorophyll\,]
LLL <- LLL[LLL$Guano==\Absent\,]

LLM<-data[data$Light==\Present\,]
LLM <- LLM[LLM$Flow.rate==\Low Flow\,]
LLM <- LLM[LLM$Chlorophyll==\Medium Chlorophyll\,]
LLM <- LLM[LLM$Guano==\Absent\,]

LLH<-data[data$Light==\Present\,]
LLH <- LLH[LLH$Flow.rate==\Low Flow\,]
LLH <- LLH[LLH$Chlorophyll==\High Chlorophyll\,]
LLH <- LLH[LLH$Guano==\Absent\,]

LLG<-data[data$Light==\Present\,]
LLG <- LLG[LLG$Flow.rate==\Low Flow\,]
LLG <- LLG[LLG$Chlorophyll==\No Chlorophyll\,]
LLG <- LLG[LLG$Guano==\Present\,]
#####################################################################

##    Light On, Medium Flow

######################################################################

LMN<-data[data$Light==\Present\,]
LMN <- LMN[LMN$Flow.rate==\Medium Flow\,]
LMN <- LMN[LMN$Chlorophyll==\No Chlorophyll\,]
LMN <- LMN[LMN$Guano==\Absent\,]

LML<-data[data$Light==\Present\,]
LML <- LML[LML$Flow.rate==\Medium Flow\,]
LML <- LML[LML$Chlorophyll==\Low Chlorophyll\,]
LML <- LML[LML$Guano==\Absent\,]

LMM<-data[data$Light==\Present\,]
LMM <- LMM[LMM$Flow.rate==\Medium Flow\,]
LMM <- LMM[LMM$Chlorophyll==\Medium Chlorophyll\,]
LMM <- LMM[LMM$Guano==\Absent\,]

LMH<-data[data$Light==\Present\,]
LMH <- LMH[LMH$Flow.rate==\Medium Flow\,]
LMH <- LMH[LMH$Chlorophyll==\High Chlorophyll\,]
LMH <- LMH[LMH$Guano==\Absent\,]

LMG<-data[data$Light==\Present\,]
LMG <- LMG[LMG$Flow.rate==\Medium Flow\,]
LMG <- LMG[LMG$Chlorophyll==\No Chlorophyll\,]
LMG <- LMG[LMG$Guano==\Present\,]
################################################################

###    Light On, High Flow

####################################################################

LHN<-data[data$Light==\Present\,]
LHN <- LHN[LHN$Flow.rate==\High Flow\,]
LHN <- LHN[LHN$Chlorophyll==\No Chlorophyll\,]
LHN <- LHN[LHN$Guano==\Absent\,]

LHL<-data[data$Light==\Present\,]
LHL <- LHL[LHL$Flow.rate==\High Flow\,]
LHL <- LHL[LHL$Chlorophyll==\Low Chlorophyll\,]
LHL <- LHL[LHL$Guano==\Absent\,]

LHM<-data[data$Light==\Present\,]
LHM <- LHM[LHM$Flow.rate==\High Flow\,]
LHM <- LHM[LHM$Chlorophyll==\Medium Chlorophyll\,]
LHM <- LHM[LHM$Guano==\Absent\,]

LHH<-data[data$Light==\Present\,]
LHH <- LHH[LHH$Flow.rate==\High Flow\,]
LHH <- LHH[LHH$Chlorophyll==\High Chlorophyll\,]
LHH <- LHH[LHH$Guano==\Absent\,]

LHG<-data[data$Light==\Present\,]
LHG <- LHG[LHG$Flow.rate==\High Flow\,]
LHG <- LHG[LHG$Chlorophyll==\No Chlorophyll\,]
LHG <- LHG[LHG$Guano==\Present\,]
################################################################


###    Light On, Extreme Flow

####################################################################

LEN<-data[data$Light==\Present\,]
LEN <- LEN[LEN$Flow.rate==\Extreme Flow\,]
LEN <- LEN[LEN$Chlorophyll==\No Chlorophyll\,]
LEN <- LEN[LEN$Guano==\Absent\,]

LEL<-data[data$Light==\Present\,]
LEL <- LEL[LEL$Flow.rate==\Extreme Flow\,]
LEL <- LEL[LEL$Chlorophyll==\Low Chlorophyll\,]
LEL <- LEL[LEL$Guano==\Absent\,]

LEM<-data[data$Light==\Present\,]
LEM <- LEM[LEM$Flow.rate==\Extreme Flow\,]
LEM <- LEM[LEM$Chlorophyll==\Medium Chlorophyll\,]
LEM <- LEM[LEM$Guano==\Absent\,]

LEH<-data[data$Light==\Present\,]
LEH <- LEH[LEH$Flow.rate==\Extreme Flow\,]
LEH <- LEH[LEH$Chlorophyll==\High Chlorophyll\,]
LEH <- LEH[LEH$Guano==\Absent\,]

LEG<-data[data$Light==\Present\,]
LEG <- LEG[LEG$Flow.rate==\Extreme Flow\,]
LEG <- LEG[LEG$Chlorophyll==\No Chlorophyll\,]
LEG <- LEG[LEG$Guano==\Present\,]
################################################################

#####################################################################

   ##  Light Off, NO Flow

####################################################################

DNN<-data[data$Light==\Absent\,]
DNN <- DNN[DNN$Flow.rate==\No Flow\,]
DNN <- DNN[DNN$Chlorophyll==\No Chlorophyll\,]
DNN <- DNN[DNN$Guano==\Absent\,]

DNL<-data[data$Light==\Absent\,]
DNL <- DNL[DNL$Flow.rate==\No Flow\,]
DNL <- DNL[DNL$Chlorophyll==\Low Chlorophyll\,]
DNL <- DNL[DNL$Guano==\Absent\,]

DNM<-data[data$Light==\Absent\,]
DNM <- DNM[DNM$Flow.rate==\No Flow\,]
DNM <- DNM[DNM$Chlorophyll==\Medium Chlorophyll\,]
DNM <- DNM[DNM$Guano==\Absent\,]

DNH <-data[data$Light==\Absent\,]
DNH <- DNH[DNH$Flow.rate==\No Flow\,]
DNH <- DNH[DNH$Chlorophyll==\High Chlorophyll\,]
DNH <- DNH[DNH$Guano==\Absent\,]

DNG<-data[data$Light==\Absent\,]
DNG <- DNG[DNG$Flow.rate==\No Flow\,]
DNG <- DNG[DNG$Chlorophyll==\No Chlorophyll\,]
DNG <- DNG[DNG$Guano==\Present\,]
##############################################################

##    Lights Off, Low Flow

###############################################################

DLN <-data[data$Light==\Absent\,]
DLN <- DLN[DLN$Flow.rate==\Low Flow\,]
DLN <- DLN[DLN$Chlorophyll==\No Chlorophyll\,]
DLN <- DLN[DLN$Guano==\Absent\,]

DLL <-data[data$Light==\Absent\,]
DLL <- DLL[DLL$Flow.rate==\Low Flow\,]
DLL <- DLL[DLL$Chlorophyll==\Low Chlorophyll\,]
DLL <- DLL[DLL$Guano==\Absent\,]

DLM <-data[data$Light==\Absent\,]
DLM <- DLM[DLM$Flow.rate==\Low Flow\,]
DLM <- DLM[DLM$Chlorophyll==\Medium Chlorophyll\,]
DLM <- DLM[DLM$Guano==\Absent\,]

DLH <-data[data$Light==\Absent\,]
DLH <- DLH[DLH$Flow.rate==\Low Flow\,]
DLH <- DLH[DLH$Chlorophyll==\High Chlorophyll\,]
DLH <- DLH[DLH$Guano==\Absent\,]

DLG <-data[data$Light==\Absent\,]
DLG <- DLG[DLG$Flow.rate==\Low Flow\,]
DLG <- DLG[DLG$Chlorophyll==\No Chlorophyll\,]
DLG <- DLG[DLG$Guano==\Present\,]
#####################################################################

##    Light Off, Medium Flow

######################################################################

DMN <-data[data$Light==\Absent\,]
DMN <- DMN[DMN$Flow.rate==\Medium Flow\,]
DMN <- DMN[DMN$Chlorophyll==\No Chlorophyll\,]
DMN <- DMN[DMN$Guano==\Absent\,]

DML <-data[data$Light==\Absent\,]
DML <- DML[DML$Flow.rate==\Medium Flow\,]
DML <- DML[DML$Chlorophyll==\Low Chlorophyll\,]
DML <- DML[DML$Guano==\Absent\,]

DMM <-data[data$Light==\Absent\,]
DMM <- DMM[DMM$Flow.rate==\Medium Flow\,]
DMM <- DMM[DMM$Chlorophyll==\Medium Chlorophyll\,]
DMM <- DMM[DMM$Guano==\Absent\,]

DMH <-data[data$Light==\Absent\,]
DMH <- DMH[DMH$Flow.rate==\Medium Flow\,]
DMH <- DMH[DMH$Chlorophyll==\High Chlorophyll\,]
DMH <- DMH[DMH$Guano==\Absent\,]

DMG <-data[data$Light==\Absent\,]
DMG <- DMG[DMG$Flow.rate==\Medium Flow\,]
DMG <- DMG[DMG$Chlorophyll==\No Chlorophyll\,]
DMG <- DMG[DMG$Guano==\Present\,]
################################################################

###    Light Off, High Flow

####################################################################

DHN<-data[data$Light==\Absent\,]
DHN <- DHN[DHN$Flow.rate==\High Flow\,]
DHN <- DHN[DHN$Chlorophyll==\No Chlorophyll\,]
DHN <- DHN[DHN$Guano==\Absent\,]

DHL<-data[data$Light==\Absent\,]
DHL <- DHL[DHL$Flow.rate==\High Flow\,]
DHL <- DHL[DHL$Chlorophyll==\Low Chlorophyll\,]
DHL <- DHL[DHL$Guano==\Absent\,]

DHM<-data[data$Light==\Absent\,]
DHM <- DHM[DHM$Flow.rate==\High Flow\,]
DHM <- DHM[DHM$Chlorophyll==\Medium Chlorophyll\,]
DHM <- DHM[DHM$Guano==\Absent\,]

DHH<-data[data$Light==\Absent\,]
DHH <- DHH[DHH$Flow.rate==\High Flow\,]
DHH <- DHH[DHH$Chlorophyll==\High Chlorophyll\,]
DHH <- DHH[DHH$Guano==\Absent\,]

DHG<-data[data$Light==\Absent\,]
DHG <- DHG[DHG$Flow.rate==\High Flow\,]
DHG <- DHG[DHG$Chlorophyll==\No Chlorophyll\,]
DHG <- DHG[DHG$Guano==\Present\,]
################################################################


###    Light Off, Extreme Flow

####################################################################

DEN<-data[data$Light==\Absent\,]
DEN <- DEN[DEN$Flow.rate==\Extreme Flow\,]
DEN <- DEN[DEN$Chlorophyll==\No Chlorophyll\,]
DEN <- DEN[DEN$Guano==\Absent\,]

DEL<-data[data$Light==\Absent\,]
DEL <- DEL[DEL$Flow.rate==\Extreme Flow\,]
DEL <- DEL[DEL$Chlorophyll==\Low Chlorophyll\,]
DEL <- DEL[DEL$Guano==\Absent\,]

DEM<-data[data$Light==\Absent\,]
DEM <- DEM[DEM$Flow.rate==\Extreme Flow\,]
DEM <- DEM[DEM$Chlorophyll==\Medium Chlorophyll\,]
DEM <- DEM[DEM$Guano==\Absent\,]

DEH<-data[data$Light==\Absent\,]
DEH <- DEH[DEH$Flow.rate==\Extreme Flow\,]
DEH <- DEH[DEH$Chlorophyll==\High Chlorophyll\,]
DEH <- DEH[DEH$Guano==\Absent\,]

DEG<-data[data$Light==\Absent\,]
DEG <- DEG[DEG$Flow.rate==\Extreme Flow\,]
DEG <- DEG[DEG$Chlorophyll==\No Chlorophyll\,]
DEG <- DEG[DEG$Guano==\Present\,]
################################################################
  ### setting data as circular
##   Lights on

LNN <- circular(LNN$turn.angle, units=\degrees\, template=\geographics\) #assign LNN subset to \LNN\ variable
LNL <- circular(LNL$turn.angle, units=\degrees\, template=\geographics\) #LNL
LNM <- circular(LNM$turn.angle, units=\degrees\, template=\geographics\) #LNM
LNH <- circular(LNH$turn.angle, units=\degrees\, template=\geographics\) #LNH
LNG <- circular(LNG$turn.angle, units=\degrees\, template=\geographics\) #LNG

LLN <- circular(LLN$turn.angle, units=\degrees\, template=\geographics\) #LLN
LLL <- circular(LLL$turn.angle, units=\degrees\, template=\geographics\) #LLL
LLM <- circular(LLM$turn.angle, units=\degrees\, template=\geographics\) #LLM
LLH <- circular(LLH$turn.angle, units=\degrees\, template=\geographics\) #LLH
LLG <- circular(LLG$turn.angle, units=\degrees\, template=\geographics\) #LLG

LMN <- circular(LMN$turn.angle, units=\degrees\, template=\geographics\) #LMN
LML <- circular(LML$turn.angle, units=\degrees\, template=\geographics\) #LML
LMM <- circular(LMM$turn.angle, units=\degrees\, template=\geographics\) #LMM
LMH <- circular(LMH$turn.angle, units=\degrees\, template=\geographics\) #LMH
LMG <- circular(LMG$turn.angle, units=\degrees\, template=\geographics\) #LMG

LHN <- circular(LHN$turn.angle, units=\degrees\, template=\geographics\) #LHN
LHL <- circular(LHL$turn.angle, units=\degrees\, template=\geographics\) #LHL
LHM <- circular(LHM$turn.angle, units=\degrees\, template=\geographics\) #LHM
LHH <- circular(LHH$turn.angle, units=\degrees\, template=\geographics\) #LHH
LHG <- circular(LHG$turn.angle, units=\degrees\, template=\geographics\) #LHG

LEN <- circular(LEN$turn.angle, units=\degrees\, template=\geographics\) #LEN
LEL <- circular(LEL$turn.angle, units=\degrees\, template=\geographics\) #LEL
LEM <- circular(LEM$turn.angle, units=\degrees\, template=\geographics\) #LEM
LEH <- circular(LEH$turn.angle, units=\degrees\, template=\geographics\) #LEH
LEG <- circular(LEG$turn.angle, units=\degrees\, template=\geographics\) #LEG

############  Lights Off

DNN <- circular(DNN$turn.angle, units=\degrees\, template=\geographics\) #DNN
DNL <- circular(DNL$turn.angle, units=\degrees\, template=\geographics\) #DNL
DNM <- circular(DNM$turn.angle, units=\degrees\, template=\geographics\) #DNM
DNH <- circular(DNH$turn.angle, units=\degrees\, template=\geographics\) #DNH
DNG <- circular(DNG$turn.angle, units=\degrees\, template=\geographics\) #DNG

DLN <- circular(DLN$turn.angle, units=\degrees\, template=\geographics\) #DLN
DLL <- circular(DLL$turn.angle, units=\degrees\, template=\geographics\) #DLL
DLM <- circular(DLM$turn.angle, units=\degrees\, template=\geographics\) #DLM
DLH <- circular(DLH$turn.angle, units=\degrees\, template=\geographics\) #DLH
DLG <- circular(DLG$turn.angle, units=\degrees\, template=\geographics\) #DLG

DMN <- circular(DMN$turn.angle, units=\degrees\, template=\geographics\) #DMN
DML <- circular(DML$turn.angle, units=\degrees\, template=\geographics\) #DML
DMM <- circular(DMM$turn.angle, units=\degrees\, template=\geographics\) #DMM
DMH <- circular(DMH$turn.angle, units=\degrees\, template=\geographics\) #DMH
DMG <- circular(DMG$turn.angle, units=\degrees\, template=\geographics\) #DMG

DHN <- circular(DHN$turn.angle, units=\degrees\, template=\geographics\) #DHN
DHL <- circular(DHL$turn.angle, units=\degrees\, template=\geographics\) #DHL
DHM <- circular(DHM$turn.angle, units=\degrees\, template=\geographics\) #DHM
DHH <- circular(DHH$turn.angle, units=\degrees\, template=\geographics\) #DHH
DHG <- circular(DHG$turn.angle, units=\degrees\, template=\geographics\) #DHG

DEN <- circular(DEN$turn.angle, units=\degrees\, template=\geographics\) #DEN
DEL <- circular(DEL$turn.angle, units=\degrees\, template=\geographics\) #DEL
DEM <- circular(DEM$turn.angle, units=\degrees\, template=\geographics\) #DEM
DEH <- circular(DEH$turn.angle, units=\degrees\, template=\geographics\) #DEH
DEG <- circular(DEG$turn.angle, units=\degrees\, template=\geographics\) #DEG

<!-- rnb-source-end -->

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<!-- rnb-text-begin -->


Now that data is subsetted, find the means... and plot circular plot with mean vector

Lights On first...


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<!-- rnb-chunk-begin -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxubWVhbihMTk4sIG5hLnJtID0gVFJVRSkgI3JlbW92ZSBOQXMgZnJvbSBkYXRhc2V0LCB0aGVuIGZpbmQgbWVhbiBcblxuYGBgXG5gYGAifQ== -->

```r
```r
mean(LNN, na.rm = TRUE) #remove NAs from dataset, then find mean 

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<!-- rnb-output-begin eyJkYXRhIjoiQ2lyY3VsYXIgRGF0YTogXG5UeXBlID0gYW5nbGVzIFxuVW5pdHMgPSBkZWdyZWVzIFxuVGVtcGxhdGUgPSBnZW9ncmFwaGljcyBcbk1vZHVsbyA9IGFzaXMgXG5aZXJvID0gMS41NzA3OTYgXG5Sb3RhdGlvbiA9IGNsb2NrIFxuWzFdIDcyLjY5Mzk1XG4ifQ== -->

Circular Data: Type = angles Units = degrees Template = geographics Modulo = asis Zero = 1.570796 Rotation = clock [1] 72.69395

<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxudmFyKExOTiwgbmEucm0gPSBUUlVFKVxuYGBgXG5gYGAifQ== -->

```r
```r
var(LNN, na.rm = TRUE)

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<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuMzg0NjU1N1xuIn0= -->

[1] 0.3846557

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxubWVhbmRldmlhdGlvbihMTk4sIG5hLnJtPVRSVUUpXG5gYGBcbmBgYCJ9 -->

```r
```r
meandeviation(LNN, na.rm=TRUE)

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<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuODExMzU4NVxuIn0= -->

[1] 0.8113585

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuc3VtbWFyeShMTk4sIG5hLnJtPVRSVUUpIFxuYGBgXG5gYGAifQ== -->

```r
```r
summary(LNN, na.rm=TRUE) 

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<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgICBuICAgICAgTWluLiAgIDFzdCBRdS4gICAgTWVkaWFuICAgICAgTWVhbiAgIDNyZCBRdS4gICAgICBNYXguICAgICAgIFJobyBcbjIuNjY4ZSswNSAxLjgwMGUrMDIgMS4yMThlKzAyIDYuODc3ZSswMSA3LjI2OWUrMDEgMi42NjhlKzAxIDAuMDAwZSswMCA2LjE1M2UtMDEgXG4gICAgIE5BJ3MgXG4yLjgwMGUrMDEgXG4ifQ== -->

    n      Min.   1st Qu.    Median      Mean   3rd Qu.      Max.       Rho 

2.668e+05 1.800e+02 1.218e+02 6.877e+01 7.269e+01 2.668e+01 0.000e+00 6.153e-01 NA’s 2.800e+01

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<!-- rnb-text-begin -->

Now for lights off AKA Dark


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```r
```r
var(DEN, na.rm = TRUE)

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<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuMTkzMjY0MVxuIn0= -->

[1] 0.1932641

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxubWVhbmRldmlhdGlvbihERU4sIG5hLnJtPVRSVUUpXG5gYGBcbmBgYCJ9 -->

```r
```r
meandeviation(DEN, na.rm=TRUE)

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<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuMzg3NDQ2N1xuIn0= -->

[1] 0.3874467

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuc3VtbWFyeShERU4sIG5hLnJtPVRSVUUpXG5gYGBcbmBgYCJ9 -->

```r
```r
summary(DEN, na.rm=TRUE)

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<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgICAgbiAgICAgICBNaW4uICAgIDFzdCBRdS4gICAgIE1lZGlhbiAgICAgICBNZWFuICAgIDNyZCBRdS4gICAgICAgTWF4LiAgICAgICAgUmhvIFxuMjAwMTAuMDAwMCAgICA5MC4wMDAwICAgICAyLjEyNzAgICAgIDAuMDAwMCAgICAgMC41MTgxICAgIC0xLjgyNTAgICAtOTAuMDAwMCAgICAgMC44MDY3IFxuIn0= -->

     n       Min.    1st Qu.     Median       Mean    3rd Qu.       Max.        Rho 

20010.0000 90.0000 2.1270 0.0000 0.5181 -1.8250 -90.0000 0.8067

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<!-- rnb-text-begin -->



Test if mean vectors are significantly different...


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```r
```r
##  A Watson Test determines if two groups’ orientations are significantly different from each other. 
watson.two.test(LNN, LEN)

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<!-- rnb-output-begin eyJkYXRhIjoiKioqIHJlY3Vyc2l2ZSBnYyBpbnZvY2F0aW9uXG4qKiogcmVjdXJzaXZlIGdjIGludm9jYXRpb25cbioqKiByZWN1cnNpdmUgZ2MgaW52b2NhdGlvblxuKioqIHJlY3Vyc2l2ZSBnYyBpbnZvY2F0aW9uXG4qKiogcmVjdXJzaXZlIGdjIGludm9jYXRpb25cbioqKiByZWN1cnNpdmUgZ2MgaW52b2NhdGlvblxuKioqIHJlY3Vyc2l2ZSBnYyBpbnZvY2F0aW9uXG4qKiogcmVjdXJzaXZlIGdjIGludm9jYXRpb25cbiJ9 -->

*** recursive gc invocation *** recursive gc invocation *** recursive gc invocation *** recursive gc invocation *** recursive gc invocation *** recursive gc invocation *** recursive gc invocation *** recursive gc invocation

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Do it all again for headings.....

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<!-- rnb-chunk-begin -->


<!-- rnb-source-begin {"data":"```r\n```r\nLNN<-data[data$Light==\\Present\\,]\nLNN <- LNN[LNN$Flow.rate==\\No Flow\\,]\nLNN <- LNN[LNN$Chlorophyll==\\No Chlorophyll\\,]\nLNN <- LNN[LNN$Guano==\\Absent\\,]\n\nLNL<-data[data$Light==\\Present\\,]\nLNL <- LNL[LNL$Flow.rate==\\No Flow\\,]\nLNL <- LNL[LNL$Chlorophyll==\\Low Chlorophyll\\,]\nLNL <- LNL[LNL$Guano==\\Absent\\,]\n\nLNM<-data[data$Light==\\Present\\,]\nLNM <- LNM[LNM$Flow.rate==\\No Flow\\,]\nLNM <- LNM[LNM$Chlorophyll==\\Medium Chlorophyll\\,]\nLNM <- LNM[LNM$Guano==\\Absent\\,]\n\nLNH<-data[data$Light==\\Present\\,]\nLNH <- LNH[LNH$Flow.rate==\\No Flow\\,]\nLNH <- LNH[LNH$Chlorophyll==\\High Chlorophyll\\,]\nLNH <- LNH[LNH$Guano==\\Absent\\,]\n\nLNG<-data[data$Light==\\Present\\,]\nLNG <- LNG[LNG$Flow.rate==\\No Flow\\,]\nLNG <- LNG[LNG$Chlorophyll==\\No Chlorophyll\\,]\nLNG <- LNG[LNG$Guano==\\Present\\,]\n##############################################################\n\n##    Lights On, Low Flow\n\n###############################################################\n\nLLN<-data[data$Light==\\Present\\,]\nLLN <- LLN[LLN$Flow.rate==\\Low Flow\\,]\nLLN <- LLN[LLN$Chlorophyll==\\No Chlorophyll\\,]\nLLN <- LLN[LLN$Guano==\\Absent\\,]\n\nLLL<-data[data$Light==\\Present\\,]\nLLL <- LLL[LLL$Flow.rate==\\Low Flow\\,]\nLLL <- LLL[LLL$Chlorophyll==\\Low Chlorophyll\\,]\nLLL <- LLL[LLL$Guano==\\Absent\\,]\n\nLLM<-data[data$Light==\\Present\\,]\nLLM <- LLM[LLM$Flow.rate==\\Low Flow\\,]\nLLM <- LLM[LLM$Chlorophyll==\\Medium Chlorophyll\\,]\nLLM <- LLM[LLM$Guano==\\Absent\\,]\n\nLLH<-data[data$Light==\\Present\\,]\nLLH <- LLH[LLH$Flow.rate==\\Low Flow\\,]\nLLH <- LLH[LLH$Chlorophyll==\\High Chlorophyll\\,]\nLLH <- LLH[LLH$Guano==\\Absent\\,]\n\nLLG<-data[data$Light==\\Present\\,]\nLLG <- LLG[LLG$Flow.rate==\\Low Flow\\,]\nLLG <- LLG[LLG$Chlorophyll==\\No Chlorophyll\\,]\nLLG <- LLG[LLG$Guano==\\Present\\,]\n#####################################################################\n\n##    Light On, Medium Flow\n\n######################################################################\n\nLMN<-data[data$Light==\\Present\\,]\nLMN <- LMN[LMN$Flow.rate==\\Medium Flow\\,]\nLMN <- LMN[LMN$Chlorophyll==\\No Chlorophyll\\,]\nLMN <- LMN[LMN$Guano==\\Absent\\,]\n\nLML<-data[data$Light==\\Present\\,]\nLML <- LML[LML$Flow.rate==\\Medium Flow\\,]\nLML <- LML[LML$Chlorophyll==\\Low Chlorophyll\\,]\nLML <- LML[LML$Guano==\\Absent\\,]\n\nLMM<-data[data$Light==\\Present\\,]\nLMM <- LMM[LMM$Flow.rate==\\Medium Flow\\,]\nLMM <- LMM[LMM$Chlorophyll==\\Medium Chlorophyll\\,]\nLMM <- LMM[LMM$Guano==\\Absent\\,]\n\nLMH<-data[data$Light==\\Present\\,]\nLMH <- LMH[LMH$Flow.rate==\\Medium Flow\\,]\nLMH <- LMH[LMH$Chlorophyll==\\High Chlorophyll\\,]\nLMH <- LMH[LMH$Guano==\\Absent\\,]\n\nLMG<-data[data$Light==\\Present\\,]\nLMG <- LMG[LMG$Flow.rate==\\Medium Flow\\,]\nLMG <- LMG[LMG$Chlorophyll==\\No Chlorophyll\\,]\nLMG <- LMG[LMG$Guano==\\Present\\,]\n################################################################\n\n###    Light On, High Flow\n\n####################################################################\n\nLHN<-data[data$Light==\\Present\\,]\nLHN <- LHN[LHN$Flow.rate==\\High Flow\\,]\nLHN <- LHN[LHN$Chlorophyll==\\No Chlorophyll\\,]\nLHN <- LHN[LHN$Guano==\\Absent\\,]\n\nLHL<-data[data$Light==\\Present\\,]\nLHL <- LHL[LHL$Flow.rate==\\High Flow\\,]\nLHL <- LHL[LHL$Chlorophyll==\\Low Chlorophyll\\,]\nLHL <- LHL[LHL$Guano==\\Absent\\,]\n\nLHM<-data[data$Light==\\Present\\,]\nLHM <- LHM[LHM$Flow.rate==\\High Flow\\,]\nLHM <- LHM[LHM$Chlorophyll==\\Medium Chlorophyll\\,]\nLHM <- LHM[LHM$Guano==\\Absent\\,]\n\nLHH<-data[data$Light==\\Present\\,]\nLHH <- LHH[LHH$Flow.rate==\\High Flow\\,]\nLHH <- LHH[LHH$Chlorophyll==\\High Chlorophyll\\,]\nLHH <- LHH[LHH$Guano==\\Absent\\,]\n\nLHG<-data[data$Light==\\Present\\,]\nLHG <- LHG[LHG$Flow.rate==\\High Flow\\,]\nLHG <- LHG[LHG$Chlorophyll==\\No Chlorophyll\\,]\nLHG <- LHG[LHG$Guano==\\Present\\,]\n################################################################\n\n\n###    Light On, Extreme Flow\n\n####################################################################\n\nLEN<-data[data$Light==\\Present\\,]\nLEN <- LEN[LEN$Flow.rate==\\Extreme Flow\\,]\nLEN <- LEN[LEN$Chlorophyll==\\No Chlorophyll\\,]\nLEN <- LEN[LEN$Guano==\\Absent\\,]\n\nLEL<-data[data$Light==\\Present\\,]\nLEL <- LEL[LEL$Flow.rate==\\Extreme Flow\\,]\nLEL <- LEL[LEL$Chlorophyll==\\Low Chlorophyll\\,]\nLEL <- LEL[LEL$Guano==\\Absent\\,]\n\nLEM<-data[data$Light==\\Present\\,]\nLEM <- LEM[LEM$Flow.rate==\\Extreme Flow\\,]\nLEM <- LEM[LEM$Chlorophyll==\\Medium Chlorophyll\\,]\nLEM <- LEM[LEM$Guano==\\Absent\\,]\n\nLEH<-data[data$Light==\\Present\\,]\nLEH <- LEH[LEH$Flow.rate==\\Extreme Flow\\,]\nLEH <- LEH[LEH$Chlorophyll==\\High Chlorophyll\\,]\nLEH <- LEH[LEH$Guano==\\Absent\\,]\n\nLEG<-data[data$Light==\\Present\\,]\nLEG <- LEG[LEG$Flow.rate==\\Extreme Flow\\,]\nLEG <- LEG[LEG$Chlorophyll==\\No Chlorophyll\\,]\nLEG <- LEG[LEG$Guano==\\Present\\,]\n################################################################\n\n#####################################################################\n\n   ##  Light Off, NO Flow\n\n####################################################################\n\nDNN<-data[data$Light==\\Absent\\,]\nDNN <- DNN[DNN$Flow.rate==\\No Flow\\,]\nDNN <- DNN[DNN$Chlorophyll==\\No Chlorophyll\\,]\nDNN <- DNN[DNN$Guano==\\Absent\\,]\n\nDNL<-data[data$Light==\\Absent\\,]\nDNL <- DNL[DNL$Flow.rate==\\No Flow\\,]\nDNL <- DNL[DNL$Chlorophyll==\\Low Chlorophyll\\,]\nDNL <- DNL[DNL$Guano==\\Absent\\,]\n\nDNM<-data[data$Light==\\Absent\\,]\nDNM <- DNM[DNM$Flow.rate==\\No Flow\\,]\nDNM <- DNM[DNM$Chlorophyll==\\Medium Chlorophyll\\,]\nDNM <- DNM[DNM$Guano==\\Absent\\,]\n\nDNH <-data[data$Light==\\Absent\\,]\nDNH <- DNH[DNH$Flow.rate==\\No Flow\\,]\nDNH <- DNH[DNH$Chlorophyll==\\High Chlorophyll\\,]\nDNH <- DNH[DNH$Guano==\\Absent\\,]\n\nDNG<-data[data$Light==\\Absent\\,]\nDNG <- DNG[DNG$Flow.rate==\\No Flow\\,]\nDNG <- DNG[DNG$Chlorophyll==\\No Chlorophyll\\,]\nDNG <- DNG[DNG$Guano==\\Present\\,]\n##############################################################\n\n##    Lights Off, Low Flow\n\n###############################################################\n\nDLN <-data[data$Light==\\Absent\\,]\nDLN <- DLN[DLN$Flow.rate==\\Low Flow\\,]\nDLN <- DLN[DLN$Chlorophyll==\\No Chlorophyll\\,]\nDLN <- DLN[DLN$Guano==\\Absent\\,]\n\nDLL <-data[data$Light==\\Absent\\,]\nDLL <- DLL[DLL$Flow.rate==\\Low Flow\\,]\nDLL <- DLL[DLL$Chlorophyll==\\Low Chlorophyll\\,]\nDLL <- DLL[DLL$Guano==\\Absent\\,]\n\nDLM <-data[data$Light==\\Absent\\,]\nDLM <- DLM[DLM$Flow.rate==\\Low Flow\\,]\nDLM <- DLM[DLM$Chlorophyll==\\Medium Chlorophyll\\,]\nDLM <- DLM[DLM$Guano==\\Absent\\,]\n\nDLH <-data[data$Light==\\Absent\\,]\nDLH <- DLH[DLH$Flow.rate==\\Low Flow\\,]\nDLH <- DLH[DLH$Chlorophyll==\\High Chlorophyll\\,]\nDLH <- DLH[DLH$Guano==\\Absent\\,]\n\nDLG <-data[data$Light==\\Absent\\,]\nDLG <- DLG[DLG$Flow.rate==\\Low Flow\\,]\nDLG <- DLG[DLG$Chlorophyll==\\No Chlorophyll\\,]\nDLG <- DLG[DLG$Guano==\\Present\\,]\n#####################################################################\n\n##    Light Off, Medium Flow\n\n######################################################################\n\nDMN <-data[data$Light==\\Absent\\,]\nDMN <- DMN[DMN$Flow.rate==\\Medium Flow\\,]\nDMN <- DMN[DMN$Chlorophyll==\\No Chlorophyll\\,]\nDMN <- DMN[DMN$Guano==\\Absent\\,]\n\nDML <-data[data$Light==\\Absent\\,]\nDML <- DML[DML$Flow.rate==\\Medium Flow\\,]\nDML <- DML[DML$Chlorophyll==\\Low Chlorophyll\\,]\nDML <- DML[DML$Guano==\\Absent\\,]\n\nDMM <-data[data$Light==\\Absent\\,]\nDMM <- DMM[DMM$Flow.rate==\\Medium Flow\\,]\nDMM <- DMM[DMM$Chlorophyll==\\Medium Chlorophyll\\,]\nDMM <- DMM[DMM$Guano==\\Absent\\,]\n\nDMH <-data[data$Light==\\Absent\\,]\nDMH <- DMH[DMH$Flow.rate==\\Medium Flow\\,]\nDMH <- DMH[DMH$Chlorophyll==\\High Chlorophyll\\,]\nDMH <- DMH[DMH$Guano==\\Absent\\,]\n\nDMG <-data[data$Light==\\Absent\\,]\nDMG <- DMG[DMG$Flow.rate==\\Medium Flow\\,]\nDMG <- DMG[DMG$Chlorophyll==\\No Chlorophyll\\,]\nDMG <- DMG[DMG$Guano==\\Present\\,]\n################################################################\n\n###    Light Off, High Flow\n\n####################################################################\n\nDHN<-data[data$Light==\\Absent\\,]\nDHN <- DHN[DHN$Flow.rate==\\High Flow\\,]\nDHN <- DHN[DHN$Chlorophyll==\\No Chlorophyll\\,]\nDHN <- DHN[DHN$Guano==\\Absent\\,]\n\nDHL<-data[data$Light==\\Absent\\,]\nDHL <- DHL[DHL$Flow.rate==\\High Flow\\,]\nDHL <- DHL[DHL$Chlorophyll==\\Low Chlorophyll\\,]\nDHL <- DHL[DHL$Guano==\\Absent\\,]\n\nDHM<-data[data$Light==\\Absent\\,]\nDHM <- DHM[DHM$Flow.rate==\\High Flow\\,]\nDHM <- DHM[DHM$Chlorophyll==\\Medium Chlorophyll\\,]\nDHM <- DHM[DHM$Guano==\\Absent\\,]\n\nDHH<-data[data$Light==\\Absent\\,]\nDHH <- DHH[DHH$Flow.rate==\\High Flow\\,]\nDHH <- DHH[DHH$Chlorophyll==\\High Chlorophyll\\,]\nDHH <- DHH[DHH$Guano==\\Absent\\,]\n\nDHG<-data[data$Light==\\Absent\\,]\nDHG <- DHG[DHG$Flow.rate==\\High Flow\\,]\nDHG <- DHG[DHG$Chlorophyll==\\No Chlorophyll\\,]\nDHG <- DHG[DHG$Guano==\\Present\\,]\n################################################################\n\n\n###    Light Off, Extreme Flow\n\n####################################################################\n\nDEN<-data[data$Light==\\Absent\\,]\nDEN <- DEN[DEN$Flow.rate==\\Extreme Flow\\,]\nDEN <- DEN[DEN$Chlorophyll==\\No Chlorophyll\\,]\nDEN <- DEN[DEN$Guano==\\Absent\\,]\n\nDEL<-data[data$Light==\\Absent\\,]\nDEL <- DEL[DEL$Flow.rate==\\Extreme Flow\\,]\nDEL <- DEL[DEL$Chlorophyll==\\Low Chlorophyll\\,]\nDEL <- DEL[DEL$Guano==\\Absent\\,]\n\nDEM<-data[data$Light==\\Absent\\,]\nDEM <- DEM[DEM$Flow.rate==\\Extreme Flow\\,]\nDEM <- DEM[DEM$Chlorophyll==\\Medium Chlorophyll\\,]\nDEM <- DEM[DEM$Guano==\\Absent\\,]\n\nDEH<-data[data$Light==\\Absent\\,]\nDEH <- DEH[DEH$Flow.rate==\\Extreme Flow\\,]\nDEH <- DEH[DEH$Chlorophyll==\\High Chlorophyll\\,]\nDEH <- DEH[DEH$Guano==\\Absent\\,]\n\nDEG<-data[data$Light==\\Absent\\,]\nDEG <- DEG[DEG$Flow.rate==\\Extreme Flow\\,]\nDEG <- DEG[DEG$Chlorophyll==\\No Chlorophyll\\,]\nDEG <- DEG[DEG$Guano==\\Present\\,]\n\n```\n```"} -->

```r
```r
LNN<-data[data$Light==\Present\,]
LNN <- LNN[LNN$Flow.rate==\No Flow\,]
LNN <- LNN[LNN$Chlorophyll==\No Chlorophyll\,]
LNN <- LNN[LNN$Guano==\Absent\,]

LNL<-data[data$Light==\Present\,]
LNL <- LNL[LNL$Flow.rate==\No Flow\,]
LNL <- LNL[LNL$Chlorophyll==\Low Chlorophyll\,]
LNL <- LNL[LNL$Guano==\Absent\,]

LNM<-data[data$Light==\Present\,]
LNM <- LNM[LNM$Flow.rate==\No Flow\,]
LNM <- LNM[LNM$Chlorophyll==\Medium Chlorophyll\,]
LNM <- LNM[LNM$Guano==\Absent\,]

LNH<-data[data$Light==\Present\,]
LNH <- LNH[LNH$Flow.rate==\No Flow\,]
LNH <- LNH[LNH$Chlorophyll==\High Chlorophyll\,]
LNH <- LNH[LNH$Guano==\Absent\,]

LNG<-data[data$Light==\Present\,]
LNG <- LNG[LNG$Flow.rate==\No Flow\,]
LNG <- LNG[LNG$Chlorophyll==\No Chlorophyll\,]
LNG <- LNG[LNG$Guano==\Present\,]
##############################################################

##    Lights On, Low Flow

###############################################################

LLN<-data[data$Light==\Present\,]
LLN <- LLN[LLN$Flow.rate==\Low Flow\,]
LLN <- LLN[LLN$Chlorophyll==\No Chlorophyll\,]
LLN <- LLN[LLN$Guano==\Absent\,]

LLL<-data[data$Light==\Present\,]
LLL <- LLL[LLL$Flow.rate==\Low Flow\,]
LLL <- LLL[LLL$Chlorophyll==\Low Chlorophyll\,]
LLL <- LLL[LLL$Guano==\Absent\,]

LLM<-data[data$Light==\Present\,]
LLM <- LLM[LLM$Flow.rate==\Low Flow\,]
LLM <- LLM[LLM$Chlorophyll==\Medium Chlorophyll\,]
LLM <- LLM[LLM$Guano==\Absent\,]

LLH<-data[data$Light==\Present\,]
LLH <- LLH[LLH$Flow.rate==\Low Flow\,]
LLH <- LLH[LLH$Chlorophyll==\High Chlorophyll\,]
LLH <- LLH[LLH$Guano==\Absent\,]

LLG<-data[data$Light==\Present\,]
LLG <- LLG[LLG$Flow.rate==\Low Flow\,]
LLG <- LLG[LLG$Chlorophyll==\No Chlorophyll\,]
LLG <- LLG[LLG$Guano==\Present\,]
#####################################################################

##    Light On, Medium Flow

######################################################################

LMN<-data[data$Light==\Present\,]
LMN <- LMN[LMN$Flow.rate==\Medium Flow\,]
LMN <- LMN[LMN$Chlorophyll==\No Chlorophyll\,]
LMN <- LMN[LMN$Guano==\Absent\,]

LML<-data[data$Light==\Present\,]
LML <- LML[LML$Flow.rate==\Medium Flow\,]
LML <- LML[LML$Chlorophyll==\Low Chlorophyll\,]
LML <- LML[LML$Guano==\Absent\,]

LMM<-data[data$Light==\Present\,]
LMM <- LMM[LMM$Flow.rate==\Medium Flow\,]
LMM <- LMM[LMM$Chlorophyll==\Medium Chlorophyll\,]
LMM <- LMM[LMM$Guano==\Absent\,]

LMH<-data[data$Light==\Present\,]
LMH <- LMH[LMH$Flow.rate==\Medium Flow\,]
LMH <- LMH[LMH$Chlorophyll==\High Chlorophyll\,]
LMH <- LMH[LMH$Guano==\Absent\,]

LMG<-data[data$Light==\Present\,]
LMG <- LMG[LMG$Flow.rate==\Medium Flow\,]
LMG <- LMG[LMG$Chlorophyll==\No Chlorophyll\,]
LMG <- LMG[LMG$Guano==\Present\,]
################################################################

###    Light On, High Flow

####################################################################

LHN<-data[data$Light==\Present\,]
LHN <- LHN[LHN$Flow.rate==\High Flow\,]
LHN <- LHN[LHN$Chlorophyll==\No Chlorophyll\,]
LHN <- LHN[LHN$Guano==\Absent\,]

LHL<-data[data$Light==\Present\,]
LHL <- LHL[LHL$Flow.rate==\High Flow\,]
LHL <- LHL[LHL$Chlorophyll==\Low Chlorophyll\,]
LHL <- LHL[LHL$Guano==\Absent\,]

LHM<-data[data$Light==\Present\,]
LHM <- LHM[LHM$Flow.rate==\High Flow\,]
LHM <- LHM[LHM$Chlorophyll==\Medium Chlorophyll\,]
LHM <- LHM[LHM$Guano==\Absent\,]

LHH<-data[data$Light==\Present\,]
LHH <- LHH[LHH$Flow.rate==\High Flow\,]
LHH <- LHH[LHH$Chlorophyll==\High Chlorophyll\,]
LHH <- LHH[LHH$Guano==\Absent\,]

LHG<-data[data$Light==\Present\,]
LHG <- LHG[LHG$Flow.rate==\High Flow\,]
LHG <- LHG[LHG$Chlorophyll==\No Chlorophyll\,]
LHG <- LHG[LHG$Guano==\Present\,]
################################################################


###    Light On, Extreme Flow

####################################################################

LEN<-data[data$Light==\Present\,]
LEN <- LEN[LEN$Flow.rate==\Extreme Flow\,]
LEN <- LEN[LEN$Chlorophyll==\No Chlorophyll\,]
LEN <- LEN[LEN$Guano==\Absent\,]

LEL<-data[data$Light==\Present\,]
LEL <- LEL[LEL$Flow.rate==\Extreme Flow\,]
LEL <- LEL[LEL$Chlorophyll==\Low Chlorophyll\,]
LEL <- LEL[LEL$Guano==\Absent\,]

LEM<-data[data$Light==\Present\,]
LEM <- LEM[LEM$Flow.rate==\Extreme Flow\,]
LEM <- LEM[LEM$Chlorophyll==\Medium Chlorophyll\,]
LEM <- LEM[LEM$Guano==\Absent\,]

LEH<-data[data$Light==\Present\,]
LEH <- LEH[LEH$Flow.rate==\Extreme Flow\,]
LEH <- LEH[LEH$Chlorophyll==\High Chlorophyll\,]
LEH <- LEH[LEH$Guano==\Absent\,]

LEG<-data[data$Light==\Present\,]
LEG <- LEG[LEG$Flow.rate==\Extreme Flow\,]
LEG <- LEG[LEG$Chlorophyll==\No Chlorophyll\,]
LEG <- LEG[LEG$Guano==\Present\,]
################################################################

#####################################################################

   ##  Light Off, NO Flow

####################################################################

DNN<-data[data$Light==\Absent\,]
DNN <- DNN[DNN$Flow.rate==\No Flow\,]
DNN <- DNN[DNN$Chlorophyll==\No Chlorophyll\,]
DNN <- DNN[DNN$Guano==\Absent\,]

DNL<-data[data$Light==\Absent\,]
DNL <- DNL[DNL$Flow.rate==\No Flow\,]
DNL <- DNL[DNL$Chlorophyll==\Low Chlorophyll\,]
DNL <- DNL[DNL$Guano==\Absent\,]

DNM<-data[data$Light==\Absent\,]
DNM <- DNM[DNM$Flow.rate==\No Flow\,]
DNM <- DNM[DNM$Chlorophyll==\Medium Chlorophyll\,]
DNM <- DNM[DNM$Guano==\Absent\,]

DNH <-data[data$Light==\Absent\,]
DNH <- DNH[DNH$Flow.rate==\No Flow\,]
DNH <- DNH[DNH$Chlorophyll==\High Chlorophyll\,]
DNH <- DNH[DNH$Guano==\Absent\,]

DNG<-data[data$Light==\Absent\,]
DNG <- DNG[DNG$Flow.rate==\No Flow\,]
DNG <- DNG[DNG$Chlorophyll==\No Chlorophyll\,]
DNG <- DNG[DNG$Guano==\Present\,]
##############################################################

##    Lights Off, Low Flow

###############################################################

DLN <-data[data$Light==\Absent\,]
DLN <- DLN[DLN$Flow.rate==\Low Flow\,]
DLN <- DLN[DLN$Chlorophyll==\No Chlorophyll\,]
DLN <- DLN[DLN$Guano==\Absent\,]

DLL <-data[data$Light==\Absent\,]
DLL <- DLL[DLL$Flow.rate==\Low Flow\,]
DLL <- DLL[DLL$Chlorophyll==\Low Chlorophyll\,]
DLL <- DLL[DLL$Guano==\Absent\,]

DLM <-data[data$Light==\Absent\,]
DLM <- DLM[DLM$Flow.rate==\Low Flow\,]
DLM <- DLM[DLM$Chlorophyll==\Medium Chlorophyll\,]
DLM <- DLM[DLM$Guano==\Absent\,]

DLH <-data[data$Light==\Absent\,]
DLH <- DLH[DLH$Flow.rate==\Low Flow\,]
DLH <- DLH[DLH$Chlorophyll==\High Chlorophyll\,]
DLH <- DLH[DLH$Guano==\Absent\,]

DLG <-data[data$Light==\Absent\,]
DLG <- DLG[DLG$Flow.rate==\Low Flow\,]
DLG <- DLG[DLG$Chlorophyll==\No Chlorophyll\,]
DLG <- DLG[DLG$Guano==\Present\,]
#####################################################################

##    Light Off, Medium Flow

######################################################################

DMN <-data[data$Light==\Absent\,]
DMN <- DMN[DMN$Flow.rate==\Medium Flow\,]
DMN <- DMN[DMN$Chlorophyll==\No Chlorophyll\,]
DMN <- DMN[DMN$Guano==\Absent\,]

DML <-data[data$Light==\Absent\,]
DML <- DML[DML$Flow.rate==\Medium Flow\,]
DML <- DML[DML$Chlorophyll==\Low Chlorophyll\,]
DML <- DML[DML$Guano==\Absent\,]

DMM <-data[data$Light==\Absent\,]
DMM <- DMM[DMM$Flow.rate==\Medium Flow\,]
DMM <- DMM[DMM$Chlorophyll==\Medium Chlorophyll\,]
DMM <- DMM[DMM$Guano==\Absent\,]

DMH <-data[data$Light==\Absent\,]
DMH <- DMH[DMH$Flow.rate==\Medium Flow\,]
DMH <- DMH[DMH$Chlorophyll==\High Chlorophyll\,]
DMH <- DMH[DMH$Guano==\Absent\,]

DMG <-data[data$Light==\Absent\,]
DMG <- DMG[DMG$Flow.rate==\Medium Flow\,]
DMG <- DMG[DMG$Chlorophyll==\No Chlorophyll\,]
DMG <- DMG[DMG$Guano==\Present\,]
################################################################

###    Light Off, High Flow

####################################################################

DHN<-data[data$Light==\Absent\,]
DHN <- DHN[DHN$Flow.rate==\High Flow\,]
DHN <- DHN[DHN$Chlorophyll==\No Chlorophyll\,]
DHN <- DHN[DHN$Guano==\Absent\,]

DHL<-data[data$Light==\Absent\,]
DHL <- DHL[DHL$Flow.rate==\High Flow\,]
DHL <- DHL[DHL$Chlorophyll==\Low Chlorophyll\,]
DHL <- DHL[DHL$Guano==\Absent\,]

DHM<-data[data$Light==\Absent\,]
DHM <- DHM[DHM$Flow.rate==\High Flow\,]
DHM <- DHM[DHM$Chlorophyll==\Medium Chlorophyll\,]
DHM <- DHM[DHM$Guano==\Absent\,]

DHH<-data[data$Light==\Absent\,]
DHH <- DHH[DHH$Flow.rate==\High Flow\,]
DHH <- DHH[DHH$Chlorophyll==\High Chlorophyll\,]
DHH <- DHH[DHH$Guano==\Absent\,]

DHG<-data[data$Light==\Absent\,]
DHG <- DHG[DHG$Flow.rate==\High Flow\,]
DHG <- DHG[DHG$Chlorophyll==\No Chlorophyll\,]
DHG <- DHG[DHG$Guano==\Present\,]
################################################################


###    Light Off, Extreme Flow

####################################################################

DEN<-data[data$Light==\Absent\,]
DEN <- DEN[DEN$Flow.rate==\Extreme Flow\,]
DEN <- DEN[DEN$Chlorophyll==\No Chlorophyll\,]
DEN <- DEN[DEN$Guano==\Absent\,]

DEL<-data[data$Light==\Absent\,]
DEL <- DEL[DEL$Flow.rate==\Extreme Flow\,]
DEL <- DEL[DEL$Chlorophyll==\Low Chlorophyll\,]
DEL <- DEL[DEL$Guano==\Absent\,]

DEM<-data[data$Light==\Absent\,]
DEM <- DEM[DEM$Flow.rate==\Extreme Flow\,]
DEM <- DEM[DEM$Chlorophyll==\Medium Chlorophyll\,]
DEM <- DEM[DEM$Guano==\Absent\,]

DEH<-data[data$Light==\Absent\,]
DEH <- DEH[DEH$Flow.rate==\Extreme Flow\,]
DEH <- DEH[DEH$Chlorophyll==\High Chlorophyll\,]
DEH <- DEH[DEH$Guano==\Absent\,]

DEG<-data[data$Light==\Absent\,]
DEG <- DEG[DEG$Flow.rate==\Extreme Flow\,]
DEG <- DEG[DEG$Chlorophyll==\No Chlorophyll\,]
DEG <- DEG[DEG$Guano==\Present\,]

<!-- rnb-source-end -->

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<!-- rnb-text-begin -->

Run Loop for each set of conditions

Below section has rm for conditions and droplevels for factors at end


<!-- rnb-text-end -->


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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuTExMIDwtIGRyb3BsZXZlbHMoTExMKVxuc3RyKExMTClcblxuYGBgXG5gYGAifQ== -->

```r
```r
LLL <- droplevels(LLL)
str(LLL)

<!-- rnb-source-end -->

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‘data.frame’: 143282 obs. of 48 variables: $ Date : chr \20191130 \20191130 \20191130 \20191130 … $ File.name : chr \20191130_view1_ \20191130_view1_ \20191130_view1_ \20191130_view1_ … $ X : num -0.0483 -0.0479 -0.0479 -0.0479 -0.0478 … $ Y : num 0.0087 0.00942 0.00995 0.01035 0.01111 … $ Z : num -0.0136 -0.0136 -0.0136 -0.0136 -0.0136 … $ Track : int 1 1 1 1 1 1 1 1 1 1 … $ View : chr \1_ \1_ \1_ \1_ … $ D_V_T : Factor w/ 16 levels \20191130_1_1,..: 1 1 1 1 1 1 1 1 1 1 … $ D_V : Factor w/ 4 levels \20191130_1,..: 1 1 1 1 1 1 1 1 1 1 … $ Flow.rate : Factor w/ 1 level Flow: 1 1 1 1 1 1 1 1 1 1 … $ Chlorophyll : Factor w/ 1 level Chlorophyll: 1 1 1 1 1 1 1 1 1 1 … $ Guano : Factor w/ 1 level : 1 1 1 1 1 1 1 1 1 1 … $ Light : Factor w/ 1 level : 1 1 1 1 1 1 1 1 1 1 … $ dx : num 0.000351 0.000097 -0.000013 0.000025 0.000556 … $ dy : num 0.000723 0.00053 0.000397 0.000758 0.000628 … $ dz : num 0 0 0 0 0 0 0 0 0 0 … $ d : num 0.000804 0.000538 0.000397 0.000758 0.000839 … $ vx : num 0.01053 0.00291 -0.00039 0.00075 0.01668 … $ vy : num 0.0217 0.0159 0.0119 0.0227 0.0188 … $ vz : num 0 0 0 0 0 0 0 0 0 0 … $ v : num 0.0241 0.0161 0.0119 0.0228 0.0252 … $ heading : num 0.4521 0.1812 -0.0327 0.033 0.7247 … $ pitch : num 0 0 0 0 0 0 0 0 0 0 … $ vel.turn.angle : num 106.81 15.52 12.26 3.76 39.63 … $ vel.flow : num 0.622 0.616 0.612 0.623 0.619 … $ trim.X : num 0.216 0.217 0.217 0.216 0.217 … $ xsmooth : num 0.216 0.216 0.216 0.216 0.216 … $ ysmooth : num 0.0157 0.0157 0.0157 0.0157 0.0157 … $ zsmooth : num -0.0136 -0.0136 -0.0136 -0.0136 -0.0136 … $ smooth.dx : num 0.00017 0 0.00001 0.00003 0.00001 … $ smooth.dy : num 0 0 0 0 0 0 0 0 0 0 … $ smooth.dz : num 0 0 0 0 0 0 0 0 0 0 … $ smooth.d : num 0.00017 0 0.00001 0.00003 0.00001 … $ smooth.vx : num 0.0051 0 0.0003 0.0009 0.0003 … $ smooth.vy : num 0 0 0 0 0 0 0 0 0 0 … $ smooth.vz : num 0 0 0 0 0 0 0 0 0 0 … $ smooth.v : num 0.0051 0 0.0003 0.0009 0.0003 … $ smooth.heading : num 1.57 0 1.57 1.57 1.57 … $ heading.pi : num 90 0 90 90 90 90 0 90 0 90 … $ smooth.pitch : num 0 0 0 0 0 0 0 0 0 0 … $ pitch.perfect : num 0 0 0 0 0 0 0 0 0 0 … $ turn.anglexysmooth : num 1.5 1.5 1.5 1.5 1.5 … $ turn.angleyzsmooth : num 2.28 2.28 2.28 2.28 2.28 … $ turn.angle.smooth : num NA NA NA NA NA NA NA NA NA NA … $ vel.turn.angle.smooth: num 62.6 NaN NaN 0 0 … $ turn.anglexy : num -1.39 -1.38 -1.37 -1.36 -1.34 … $ turn.angleyz : num 2.57 2.53 2.51 2.49 2.46 … $ turn.angle : num 106.81 15.52 12.26 3.76 39.63 … - attr(, .action)= ‘omit’ Named int [1:1273476] 1 2 3 4 5 6 7 8 9 10 … ..- attr(, )= chr [1:1273476] \19 \31 \47 \56 …

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```r
```r
library(dplyr)
LLL_calculate <- data.frame()
for (dvt in unique(LLL$D_V_T)) {
  data <- LLL %>% subset(D_V_T==dvt)
  for (i in 2:nrow(data)) {
    subdata <- data[((i-1):i),]
    rel_point_x <- subdata[1,\X\]
    rel_point_y <- subdata[1,\Y\]
    cal_point_x <- subdata[2,\X\]
    cal_point_y <- subdata[2,\Y\]
    slope <- (cal_point_y- rel_point_y)/(cal_point_x- rel_point_x)
    angle <- atan(slope)*360/pi
    tmp <- data.frame(D_V_T=dvt,i,rel_point_x,rel_point_y,cal_point_x,cal_point_y,slope,angle)
    LLL_calculate <- rbind(LLL_calculate,tmp)
  }
  print(dvt)
}

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[1] \20191130_1__1
[1] \20191130_1__2
[1] \20191130_1__3
[1] \20191130_1__4
[1] \20191130_2__1
[1] \20191130_2__2
[1] \20191130_2__3
[1] \20191130_2__4
[1] \20191202_1__1
[1] \20191202_1__2
[1] \20191202_1__3
[1] \20191202_1__4
[1] \20191202_4__1
[1] \20191202_4__2
[1] \20191202_4__3
[1] \20191202_4__4

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Above section has rm for conditions and droplevels for factors at end


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-->

```r
```r
##############################################################################
 ### setting data as circular
##   Lights on

head(LNN)

LNN <- circular(LNN$heading.pi, units=\degrees\, template=\geographics\) #assign LNN subset to \LNN\ variable
LNL <- circular(LNL$heading.pi, units=\degrees\, template=\geographics\) #LNL
LNM <- circular(LNM$heading.pi, units=\degrees\, template=\geographics\) #LNM
LNH <- circular(LNH$heading.pi, units=\degrees\, template=\geographics\) #LNH
LNG <- circular(LNG$heading.pi, units=\degrees\, template=\geographics\) #LNG

LLN <- circular(LLN$heading.pi, units=\degrees\, template=\geographics\) #LLN
LLL <- circular(LLL$heading.pi, units=\degrees\, template=\geographics\) #LLL
LLM <- circular(LLM$heading.pi, units=\degrees\, template=\geographics\) #LLM
LLH <- circular(LLH$heading.pi, units=\degrees\, template=\geographics\) #LLH
LLG <- circular(LLG$heading.pi, units=\degrees\, template=\geographics\) #LLG

LMN <- circular(LMN$heading.pi, units=\degrees\, template=\geographics\) #LMN
LML <- circular(LML$heading.pi, units=\degrees\, template=\geographics\) #LML
LMM <- circular(LMM$heading.pi, units=\degrees\, template=\geographics\) #LMM
LMH <- circular(LMH$heading.pi, units=\degrees\, template=\geographics\) #LMH
LMG <- circular(LMG$heading.pi, units=\degrees\, template=\geographics\) #LMG

LHN <- circular(LHN$heading.pi, units=\degrees\, template=\geographics\) #LHN
LHL <- circular(LHL$heading.pi, units=\degrees\, template=\geographics\) #LHL
LHM <- circular(LHM$heading.pi, units=\degrees\, template=\geographics\) #LHM
LHH <- circular(LHH$heading.pi, units=\degrees\, template=\geographics\) #LHH
LHG <- circular(LHG$heading.pi, units=\degrees\, template=\geographics\) #LHG

LEN <- circular(LEN$heading.pi, units=\degrees\, template=\geographics\) #LEN
LEL <- circular(LEL$heading.pi, units=\degrees\, template=\geographics\) #LEL
LEM <- circular(LEM$heading.pi, units=\degrees\, template=\geographics\) #LEM
LEH <- circular(LEH$heading.pi, units=\degrees\, template=\geographics\) #LEH
LEG <- circular(LEG$heading.pi, units=\degrees\, template=\geographics\) #LEG

############  Lights Off

DNN <- circular(DNN$heading.pi, units=\degrees\, template=\geographics\) #DNN
DNL <- circular(DNL$heading.pi, units=\degrees\, template=\geographics\) #DNL
DNM <- circular(DNM$heading.pi, units=\degrees\, template=\geographics\) #DNM
DNH <- circular(DNH$heading.pi, units=\degrees\, template=\geographics\) #DNH
DNG <- circular(DNG$heading.pi, units=\degrees\, template=\geographics\) #DNG

DLN <- circular(DLN$heading.pi, units=\degrees\, template=\geographics\) #DLN
DLL <- circular(DLL$heading.pi, units=\degrees\, template=\geographics\) #DLL
DLM <- circular(DLM$heading.pi, units=\degrees\, template=\geographics\) #DLM
DLH <- circular(DLH$heading.pi, units=\degrees\, template=\geographics\) #DLH
DLG <- circular(DLG$heading.pi, units=\degrees\, template=\geographics\) #DLG

DMN <- circular(DMN$heading.pi, units=\degrees\, template=\geographics\) #DMN
DML <- circular(DML$heading.pi, units=\degrees\, template=\geographics\) #DML
DMM <- circular(DMM$heading.pi, units=\degrees\, template=\geographics\) #DMM
DMH <- circular(DMH$heading.pi, units=\degrees\, template=\geographics\) #DMH
DMG <- circular(DMG$heading.pi, units=\degrees\, template=\geographics\) #DMG

DHN <- circular(DHN$heading.pi, units=\degrees\, template=\geographics\) #DHN
DHL <- circular(DHL$heading.pi, units=\degrees\, template=\geographics\) #DHL
DHM <- circular(DHM$heading.pi, units=\degrees\, template=\geographics\) #DHM
DHH <- circular(DHH$heading.pi, units=\degrees\, template=\geographics\) #DHH
DHG <- circular(DHG$heading.pi, units=\degrees\, template=\geographics\) #DHG

DEN <- circular(DEN$heading.pi, units=\degrees\, template=\geographics\) #DEN
DEL <- circular(DEL$heading.pi, units=\degrees\, template=\geographics\) #DEL
DEM <- circular(DEM$heading.pi, units=\degrees\, template=\geographics\) #DEM
DEH <- circular(DEH$heading.pi, units=\degrees\, template=\geographics\) #DEH
DEG <- circular(DEG$heading.pi, units=\degrees\, template=\geographics\) #DEG
######################################################################################

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<!-- rnb-source-begin {"data":"```r\n```r\n############################################################################\nmean(LNN, na.rm = TRUE) #remove NAs from dataset, then find mean \nLNN.mean <- mean(LNN, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LNN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)  ### larger stack number = more zoomed out\narrows.circular(LNN.mean) #add mean to plot\n###########################################################################\n\nmean(LNL, na.rm = TRUE) #remove NAs from dataset, then find mean \nLNL.mean <- mean(LNL, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LNL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LNL.mean) #add mean to plot\n###########################################################################\n\nmean(LNM, na.rm = TRUE) #remove NAs from dataset, then find mean \nLNM.mean <- mean(LNM, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LNM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LNM.mean) #add mean to plot\n###########################################################################\n\nmean(LNH, na.rm = TRUE) #remove NAs from dataset, then find mean \nLNH.mean <- mean(LNH, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LNH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LNH.mean) #add mean to plot\n###########################################################################\n\nmean(LNG, na.rm = TRUE) #remove NAs from dataset, then find mean \nLNG.mean <- mean(LNG, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LNG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LNG.mean) #add mean to plot\n###########################################################################\n\n#########################################################################\n      ## Lights On Low Flow\n\n############################################################################\nmean(LLN, na.rm = TRUE) #remove NAs from dataset, then find mean \nLLN.mean <- mean(LLN, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LLN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LLN.mean) #add mean to plot\n###########################################################################\n\nmean(LLL, na.rm = TRUE) #remove NAs from dataset, then find mean \nLLL.mean <- mean(LLL, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LLL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LLL.mean) #add mean to plot\n###########################################################################\n\nmean(LLM, na.rm = TRUE) #remove NAs from dataset, then find mean \nLLM.mean <- mean(LLM, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LLM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LLM.mean) #add mean to plot\n###########################################################################\n\nmean(LLH, na.rm = TRUE) #remove NAs from dataset, then find mean \nLLH.mean <- mean(LLH, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LLH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LLH.mean) #add mean to plot\n###########################################################################\n\nmean(LLG, na.rm = TRUE) #remove NAs from dataset, then find mean \nLLG.mean <- mean(LLG, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LLG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LLG.mean) #add mean to plot\n###########################################################################\n\n\n#########################################################################\n      ## Lights On Medium Flow\n\n############################################################################\nmean(LMN, na.rm = TRUE) #remove NAs from dataset, then find mean \nLMN.mean <- mean(LMN, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LMN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LMN.mean) #add mean to plot\n###########################################################################\n\nmean(LML, na.rm = TRUE) #remove NAs from dataset, then find mean \nLML.mean <- mean(LML, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LML, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LML.mean) #add mean to plot\n###########################################################################\n\nmean(LMM, na.rm = TRUE) #remove NAs from dataset, then find mean \nLMM.mean <- mean(LMM, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LMM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LMM.mean) #add mean to plot\n###########################################################################\n\nmean(LMH, na.rm = TRUE) #remove NAs from dataset, then find mean \nLMH.mean <- mean(LMH, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LMH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LMH.mean) #add mean to plot\n###########################################################################\n\nmean(LMG, na.rm = TRUE) #remove NAs from dataset, then find mean \nLMG.mean <- mean(LMG, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LMG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LMG.mean) #add mean to plot\n###########################################################################\n\n\n#########################################################################\n      ## Lights On High Flow\n\n############################################################################\nmean(LHN, na.rm = TRUE) #remove NAs from dataset, then find mean \nLHN.mean <- mean(LHN, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LHN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LHN.mean) #add mean to plot\n###########################################################################\n\nmean(LHL, na.rm = TRUE) #remove NAs from dataset, then find mean \nLHL.mean <- mean(LHL, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LHL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LHL.mean) #add mean to plot\n###########################################################################\n\nmean(LHM, na.rm = TRUE) #remove NAs from dataset, then find mean \nLHM.mean <- mean(LHM, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LHM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LHM.mean) #add mean to plot\n###########################################################################\n\nmean(LHH, na.rm = TRUE) #remove NAs from dataset, then find mean \nLHH.mean <- mean(LHH, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LHH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LHH.mean) #add mean to plot\n###########################################################################\n\nmean(LHG, na.rm = TRUE) #remove NAs from dataset, then find mean \nLHG.mean <- mean(LHG, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LHG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LHG.mean) #add mean to plot\n###########################################################################\n\n\n\n#########################################################################\n      ## Lights On Extreme Flow\n\n############################################################################\nmean(LEN, na.rm = TRUE) #remove NAs from dataset, then find mean \nLEN.mean <- mean(LEN, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LEN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LEN.mean) #add mean to plot\n###########################################################################\n\nmean(LEL, na.rm = TRUE) #remove NAs from dataset, then find mean \nLEL.mean <- mean(LEL, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LEL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LEL.mean) #add mean to plot\n###########################################################################\n\nmean(LEM, na.rm = TRUE) #remove NAs from dataset, then find mean \nLEM.mean <- mean(LEM, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LEM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LEM.mean) #add mean to plot\n###########################################################################\n\nmean(LEH, na.rm = TRUE) #remove NAs from dataset, then find mean \nLEH.mean <- mean(LEH, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LEH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LEH.mean) #add mean to plot\n###########################################################################\n\nmean(LEG, na.rm = TRUE) #remove NAs from dataset, then find mean \nLEG.mean <- mean(LEG, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LEG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LEG.mean) #add mean to plot\n###########################################################################\n\n#########################################################################\n      ## Lights Off No Flow\n\n############################################################################\nmean(DNN, na.rm = TRUE) #remove NAs from dataset, then find mean \nDNN.mean <- mean(DNN, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DNN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)  ### larger stack number = more zoomed out\narrows.circular(DNN.mean) #add mean to plot\n###########################################################################\n\nmean(DNL, na.rm = TRUE) #remove NAs from dataset, then find mean \nDNL.mean <- mean(DNL, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DNL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DNL.mean) #add mean to plot\n###########################################################################\n\nmean(DNM, na.rm = TRUE) #remove NAs from dataset, then find mean \nDNM.mean <- mean(DNM, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DNM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DNM.mean) #add mean to plot\n###########################################################################\n\nmean(DNH, na.rm = TRUE) #remove NAs from dataset, then find mean \nDNH.mean <- mean(DNH, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DNH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DNH.mean) #add mean to plot\n###########################################################################\n\nmean(DNG, na.rm = TRUE) #remove NAs from dataset, then find mean \nDNG.mean <- mean(DNG, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DNG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DNG.mean) #add mean to plot\n###########################################################################\n\n#########################################################################\n      ## Lights Off Low Flow\n\n############################################################################\nmean(DLN, na.rm = TRUE) #remove NAs from dataset, then find mean \nDLN.mean <- mean(DLN, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DLN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DLN.mean) #add mean to plot\n###########################################################################\n\nmean(DLL, na.rm = TRUE) #remove NAs from dataset, then find mean \nDLL.mean <- mean(DLL, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DLL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DLL.mean) #add mean to plot\n###########################################################################\n\nmean(DLM, na.rm = TRUE) #remove NAs from dataset, then find mean \nDLM.mean <- mean(DLM, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DLM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DLM.mean) #add mean to plot\n###########################################################################\n\nmean(DLH, na.rm = TRUE) #remove NAs from dataset, then find mean \nDLH.mean <- mean(DLH, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DLH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DLH.mean) #add mean to plot\n###########################################################################\n\nmean(DLG, na.rm = TRUE) #remove NAs from dataset, then find mean \nDLG.mean <- mean(DLG, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DLG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DLG.mean) #add mean to plot\n###########################################################################\n\n\n#########################################################################\n      ## Lights On Medium Flow\n\n############################################################################\nmean(DMN, na.rm = TRUE) #remove NAs from dataset, then find mean \nDMN.mean <- mean(DMN, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DMN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DMN.mean) #add mean to plot\n###########################################################################\n\nmean(DML, na.rm = TRUE) #remove NAs from dataset, then find mean \nDML.mean <- mean(DML, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DML, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DML.mean) #add mean to plot\n###########################################################################\n\nmean(DMM, na.rm = TRUE) #remove NAs from dataset, then find mean \nDMM.mean <- mean(DMM, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DMM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DMM.mean) #add mean to plot\n###########################################################################\n\nmean(DMH, na.rm = TRUE) #remove NAs from dataset, then find mean \nDMH.mean <- mean(DMH, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DMH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DMH.mean) #add mean to plot\n###########################################################################\n\nmean(DMG, na.rm = TRUE) #remove NAs from dataset, then find mean \nDMG.mean <- mean(DMG, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DMG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DMG.mean) #add mean to plot\n###########################################################################\n\n\n#########################################################################\n      ## Lights Off High Flow\n\n############################################################################\nmean(DHN, na.rm = TRUE) #remove NAs from dataset, then find mean \nDHN.mean <- mean(DHN, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DHN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DHN.mean) #add mean to plot\n###########################################################################\n\nmean(DHL, na.rm = TRUE) #remove NAs from dataset, then find mean \nDHL.mean <- mean(DHL, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DHL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DHL.mean) #add mean to plot\n###########################################################################\n\nmean(DHM, na.rm = TRUE) #remove NAs from dataset, then find mean \nDHM.mean <- mean(DHM, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DHM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DHM.mean) #add mean to plot\n###########################################################################\n\nmean(DHH, na.rm = TRUE) #remove NAs from dataset, then find mean \nDHH.mean <- mean(DHH, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DHH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DHH.mean) #add mean to plot\n###########################################################################\n\nmean(DHG, na.rm = TRUE) #remove NAs from dataset, then find mean \nDHG.mean <- mean(DHG, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DHG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DHG.mean) #add mean to plot\n###########################################################################\n\n#########################################################################\n      ## Lights On Extreme Flow\n\n############################################################################\nmean(DEN, na.rm = TRUE) #remove NAs from dataset, then find mean \nDEN.mean <- mean(DEN, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DEN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DEN.mean) #add mean to plot\n###########################################################################\n\nmean(DEL, na.rm = TRUE) #remove NAs from dataset, then find mean \nDEL.mean <- mean(DEL, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DEL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DEL.mean) #add mean to plot\n###########################################################################\n\nmean(DEM, na.rm = TRUE) #remove NAs from dataset, then find mean \nDEM.mean <- mean(DEM, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DEM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DEM.mean) #add mean to plot\n###########################################################################\n\nmean(DEH, na.rm = TRUE) #remove NAs from dataset, then find mean \nDEH.mean <- mean(DEH, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DEH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DEH.mean) #add mean to plot\n###########################################################################\n\nmean(DEG, na.rm = TRUE) #remove NAs from dataset, then find mean \nDEG.mean <- mean(DEG, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DEG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)  ### larger stack number = more zoomed out\narrows.circular(DEG.mean) #add mean to plot\n###########################################################################\n```\n```"} -->

```r
```r
############################################################################
mean(LNN, na.rm = TRUE) #remove NAs from dataset, then find mean 
LNN.mean <- mean(LNN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LNN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)  ### larger stack number = more zoomed out
arrows.circular(LNN.mean) #add mean to plot
###########################################################################

mean(LNL, na.rm = TRUE) #remove NAs from dataset, then find mean 
LNL.mean <- mean(LNL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LNL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LNL.mean) #add mean to plot
###########################################################################

mean(LNM, na.rm = TRUE) #remove NAs from dataset, then find mean 
LNM.mean <- mean(LNM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LNM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LNM.mean) #add mean to plot
###########################################################################

mean(LNH, na.rm = TRUE) #remove NAs from dataset, then find mean 
LNH.mean <- mean(LNH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LNH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LNH.mean) #add mean to plot
###########################################################################

mean(LNG, na.rm = TRUE) #remove NAs from dataset, then find mean 
LNG.mean <- mean(LNG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LNG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LNG.mean) #add mean to plot
###########################################################################

#########################################################################
      ## Lights On Low Flow

############################################################################
mean(LLN, na.rm = TRUE) #remove NAs from dataset, then find mean 
LLN.mean <- mean(LLN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LLN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LLN.mean) #add mean to plot
###########################################################################

mean(LLL, na.rm = TRUE) #remove NAs from dataset, then find mean 
LLL.mean <- mean(LLL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LLL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LLL.mean) #add mean to plot
###########################################################################

mean(LLM, na.rm = TRUE) #remove NAs from dataset, then find mean 
LLM.mean <- mean(LLM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LLM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LLM.mean) #add mean to plot
###########################################################################

mean(LLH, na.rm = TRUE) #remove NAs from dataset, then find mean 
LLH.mean <- mean(LLH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LLH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LLH.mean) #add mean to plot
###########################################################################

mean(LLG, na.rm = TRUE) #remove NAs from dataset, then find mean 
LLG.mean <- mean(LLG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LLG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LLG.mean) #add mean to plot
###########################################################################


#########################################################################
      ## Lights On Medium Flow

############################################################################
mean(LMN, na.rm = TRUE) #remove NAs from dataset, then find mean 
LMN.mean <- mean(LMN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LMN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LMN.mean) #add mean to plot
###########################################################################

mean(LML, na.rm = TRUE) #remove NAs from dataset, then find mean 
LML.mean <- mean(LML, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LML, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LML.mean) #add mean to plot
###########################################################################

mean(LMM, na.rm = TRUE) #remove NAs from dataset, then find mean 
LMM.mean <- mean(LMM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LMM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LMM.mean) #add mean to plot
###########################################################################

mean(LMH, na.rm = TRUE) #remove NAs from dataset, then find mean 
LMH.mean <- mean(LMH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LMH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LMH.mean) #add mean to plot
###########################################################################

mean(LMG, na.rm = TRUE) #remove NAs from dataset, then find mean 
LMG.mean <- mean(LMG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LMG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LMG.mean) #add mean to plot
###########################################################################


#########################################################################
      ## Lights On High Flow

############################################################################
mean(LHN, na.rm = TRUE) #remove NAs from dataset, then find mean 
LHN.mean <- mean(LHN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LHN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LHN.mean) #add mean to plot
###########################################################################

mean(LHL, na.rm = TRUE) #remove NAs from dataset, then find mean 
LHL.mean <- mean(LHL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LHL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LHL.mean) #add mean to plot
###########################################################################

mean(LHM, na.rm = TRUE) #remove NAs from dataset, then find mean 
LHM.mean <- mean(LHM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LHM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LHM.mean) #add mean to plot
###########################################################################

mean(LHH, na.rm = TRUE) #remove NAs from dataset, then find mean 
LHH.mean <- mean(LHH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LHH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LHH.mean) #add mean to plot
###########################################################################

mean(LHG, na.rm = TRUE) #remove NAs from dataset, then find mean 
LHG.mean <- mean(LHG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LHG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LHG.mean) #add mean to plot
###########################################################################



#########################################################################
      ## Lights On Extreme Flow

############################################################################
mean(LEN, na.rm = TRUE) #remove NAs from dataset, then find mean 
LEN.mean <- mean(LEN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LEN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LEN.mean) #add mean to plot
###########################################################################

mean(LEL, na.rm = TRUE) #remove NAs from dataset, then find mean 
LEL.mean <- mean(LEL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LEL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LEL.mean) #add mean to plot
###########################################################################

mean(LEM, na.rm = TRUE) #remove NAs from dataset, then find mean 
LEM.mean <- mean(LEM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LEM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LEM.mean) #add mean to plot
###########################################################################

mean(LEH, na.rm = TRUE) #remove NAs from dataset, then find mean 
LEH.mean <- mean(LEH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LEH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LEH.mean) #add mean to plot
###########################################################################

mean(LEG, na.rm = TRUE) #remove NAs from dataset, then find mean 
LEG.mean <- mean(LEG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LEG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LEG.mean) #add mean to plot
###########################################################################

#########################################################################
      ## Lights Off No Flow

############################################################################
mean(DNN, na.rm = TRUE) #remove NAs from dataset, then find mean 
DNN.mean <- mean(DNN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DNN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)  ### larger stack number = more zoomed out
arrows.circular(DNN.mean) #add mean to plot
###########################################################################

mean(DNL, na.rm = TRUE) #remove NAs from dataset, then find mean 
DNL.mean <- mean(DNL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DNL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DNL.mean) #add mean to plot
###########################################################################

mean(DNM, na.rm = TRUE) #remove NAs from dataset, then find mean 
DNM.mean <- mean(DNM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DNM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DNM.mean) #add mean to plot
###########################################################################

mean(DNH, na.rm = TRUE) #remove NAs from dataset, then find mean 
DNH.mean <- mean(DNH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DNH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DNH.mean) #add mean to plot
###########################################################################

mean(DNG, na.rm = TRUE) #remove NAs from dataset, then find mean 
DNG.mean <- mean(DNG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DNG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DNG.mean) #add mean to plot
###########################################################################

#########################################################################
      ## Lights Off Low Flow

############################################################################
mean(DLN, na.rm = TRUE) #remove NAs from dataset, then find mean 
DLN.mean <- mean(DLN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DLN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DLN.mean) #add mean to plot
###########################################################################

mean(DLL, na.rm = TRUE) #remove NAs from dataset, then find mean 
DLL.mean <- mean(DLL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DLL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DLL.mean) #add mean to plot
###########################################################################

mean(DLM, na.rm = TRUE) #remove NAs from dataset, then find mean 
DLM.mean <- mean(DLM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DLM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DLM.mean) #add mean to plot
###########################################################################

mean(DLH, na.rm = TRUE) #remove NAs from dataset, then find mean 
DLH.mean <- mean(DLH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DLH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DLH.mean) #add mean to plot
###########################################################################

mean(DLG, na.rm = TRUE) #remove NAs from dataset, then find mean 
DLG.mean <- mean(DLG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DLG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DLG.mean) #add mean to plot
###########################################################################


#########################################################################
      ## Lights On Medium Flow

############################################################################
mean(DMN, na.rm = TRUE) #remove NAs from dataset, then find mean 
DMN.mean <- mean(DMN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DMN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DMN.mean) #add mean to plot
###########################################################################

mean(DML, na.rm = TRUE) #remove NAs from dataset, then find mean 
DML.mean <- mean(DML, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DML, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DML.mean) #add mean to plot
###########################################################################

mean(DMM, na.rm = TRUE) #remove NAs from dataset, then find mean 
DMM.mean <- mean(DMM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DMM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DMM.mean) #add mean to plot
###########################################################################

mean(DMH, na.rm = TRUE) #remove NAs from dataset, then find mean 
DMH.mean <- mean(DMH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DMH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DMH.mean) #add mean to plot
###########################################################################

mean(DMG, na.rm = TRUE) #remove NAs from dataset, then find mean 
DMG.mean <- mean(DMG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DMG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DMG.mean) #add mean to plot
###########################################################################


#########################################################################
      ## Lights Off High Flow

############################################################################
mean(DHN, na.rm = TRUE) #remove NAs from dataset, then find mean 
DHN.mean <- mean(DHN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DHN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DHN.mean) #add mean to plot
###########################################################################

mean(DHL, na.rm = TRUE) #remove NAs from dataset, then find mean 
DHL.mean <- mean(DHL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DHL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DHL.mean) #add mean to plot
###########################################################################

mean(DHM, na.rm = TRUE) #remove NAs from dataset, then find mean 
DHM.mean <- mean(DHM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DHM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DHM.mean) #add mean to plot
###########################################################################

mean(DHH, na.rm = TRUE) #remove NAs from dataset, then find mean 
DHH.mean <- mean(DHH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DHH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DHH.mean) #add mean to plot
###########################################################################

mean(DHG, na.rm = TRUE) #remove NAs from dataset, then find mean 
DHG.mean <- mean(DHG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DHG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DHG.mean) #add mean to plot
###########################################################################

#########################################################################
      ## Lights On Extreme Flow

############################################################################
mean(DEN, na.rm = TRUE) #remove NAs from dataset, then find mean 
DEN.mean <- mean(DEN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DEN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DEN.mean) #add mean to plot
###########################################################################

mean(DEL, na.rm = TRUE) #remove NAs from dataset, then find mean 
DEL.mean <- mean(DEL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DEL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DEL.mean) #add mean to plot
###########################################################################

mean(DEM, na.rm = TRUE) #remove NAs from dataset, then find mean 
DEM.mean <- mean(DEM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DEM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DEM.mean) #add mean to plot
###########################################################################

mean(DEH, na.rm = TRUE) #remove NAs from dataset, then find mean 
DEH.mean <- mean(DEH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DEH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DEH.mean) #add mean to plot
###########################################################################

mean(DEG, na.rm = TRUE) #remove NAs from dataset, then find mean 
DEG.mean <- mean(DEG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DEG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)  ### larger stack number = more zoomed out
arrows.circular(DEG.mean) #add mean to plot
###########################################################################

<!-- rnb-source-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


and pitch......

<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-source-begin {"data":"```r\n```r\nLNN<-data[data$Light==\\Present\\,]\nLNN <- LNN[LNN$Flow.rate==\\No Flow\\,]\nLNN <- LNN[LNN$Chlorophyll==\\No Chlorophyll\\,]\nLNN <- LNN[LNN$Guano==\\Absent\\,]\n\nLNL<-data[data$Light==\\Present\\,]\nLNL <- LNL[LNL$Flow.rate==\\No Flow\\,]\nLNL <- LNL[LNL$Chlorophyll==\\Low Chlorophyll\\,]\nLNL <- LNL[LNL$Guano==\\Absent\\,]\n\nLNM<-data[data$Light==\\Present\\,]\nLNM <- LNM[LNM$Flow.rate==\\No Flow\\,]\nLNM <- LNM[LNM$Chlorophyll==\\Medium Chlorophyll\\,]\nLNM <- LNM[LNM$Guano==\\Absent\\,]\n\nLNH<-data[data$Light==\\Present\\,]\nLNH <- LNH[LNH$Flow.rate==\\No Flow\\,]\nLNH <- LNH[LNH$Chlorophyll==\\High Chlorophyll\\,]\nLNH <- LNH[LNH$Guano==\\Absent\\,]\n\nLNG<-data[data$Light==\\Present\\,]\nLNG <- LNG[LNG$Flow.rate==\\No Flow\\,]\nLNG <- LNG[LNG$Chlorophyll==\\No Chlorophyll\\,]\nLNG <- LNG[LNG$Guano==\\Present\\,]\n##############################################################\n\n##    Lights On, Low Flow\n\n###############################################################\n\nLLN<-data[data$Light==\\Present\\,]\nLLN <- LLN[LLN$Flow.rate==\\Low Flow\\,]\nLLN <- LLN[LLN$Chlorophyll==\\No Chlorophyll\\,]\nLLN <- LLN[LLN$Guano==\\Absent\\,]\n\nLLL<-data[data$Light==\\Present\\,]\nLLL <- LLL[LLL$Flow.rate==\\Low Flow\\,]\nLLL <- LLL[LLL$Chlorophyll==\\Low Chlorophyll\\,]\nLLL <- LLL[LLL$Guano==\\Absent\\,]\n\nLLM<-data[data$Light==\\Present\\,]\nLLM <- LLM[LLM$Flow.rate==\\Low Flow\\,]\nLLM <- LLM[LLM$Chlorophyll==\\Medium Chlorophyll\\,]\nLLM <- LLM[LLM$Guano==\\Absent\\,]\n\nLLH<-data[data$Light==\\Present\\,]\nLLH <- LLH[LLH$Flow.rate==\\Low Flow\\,]\nLLH <- LLH[LLH$Chlorophyll==\\High Chlorophyll\\,]\nLLH <- LLH[LLH$Guano==\\Absent\\,]\n\nLLG<-data[data$Light==\\Present\\,]\nLLG <- LLG[LLG$Flow.rate==\\Low Flow\\,]\nLLG <- LLG[LLG$Chlorophyll==\\No Chlorophyll\\,]\nLLG <- LLG[LLG$Guano==\\Present\\,]\n#####################################################################\n\n##    Light On, Medium Flow\n\n######################################################################\n\nLMN<-data[data$Light==\\Present\\,]\nLMN <- LMN[LMN$Flow.rate==\\Medium Flow\\,]\nLMN <- LMN[LMN$Chlorophyll==\\No Chlorophyll\\,]\nLMN <- LMN[LMN$Guano==\\Absent\\,]\n\nLML<-data[data$Light==\\Present\\,]\nLML <- LML[LML$Flow.rate==\\Medium Flow\\,]\nLML <- LML[LML$Chlorophyll==\\Low Chlorophyll\\,]\nLML <- LML[LML$Guano==\\Absent\\,]\n\nLMM<-data[data$Light==\\Present\\,]\nLMM <- LMM[LMM$Flow.rate==\\Medium Flow\\,]\nLMM <- LMM[LMM$Chlorophyll==\\Medium Chlorophyll\\,]\nLMM <- LMM[LMM$Guano==\\Absent\\,]\n\nLMH<-data[data$Light==\\Present\\,]\nLMH <- LMH[LMH$Flow.rate==\\Medium Flow\\,]\nLMH <- LMH[LMH$Chlorophyll==\\High Chlorophyll\\,]\nLMH <- LMH[LMH$Guano==\\Absent\\,]\n\nLMG<-data[data$Light==\\Present\\,]\nLMG <- LMG[LMG$Flow.rate==\\Medium Flow\\,]\nLMG <- LMG[LMG$Chlorophyll==\\No Chlorophyll\\,]\nLMG <- LMG[LMG$Guano==\\Present\\,]\n################################################################\n\n###    Light On, High Flow\n\n####################################################################\n\nLHN<-data[data$Light==\\Present\\,]\nLHN <- LHN[LHN$Flow.rate==\\High Flow\\,]\nLHN <- LHN[LHN$Chlorophyll==\\No Chlorophyll\\,]\nLHN <- LHN[LHN$Guano==\\Absent\\,]\n\nLHL<-data[data$Light==\\Present\\,]\nLHL <- LHL[LHL$Flow.rate==\\High Flow\\,]\nLHL <- LHL[LHL$Chlorophyll==\\Low Chlorophyll\\,]\nLHL <- LHL[LHL$Guano==\\Absent\\,]\n\nLHM<-data[data$Light==\\Present\\,]\nLHM <- LHM[LHM$Flow.rate==\\High Flow\\,]\nLHM <- LHM[LHM$Chlorophyll==\\Medium Chlorophyll\\,]\nLHM <- LHM[LHM$Guano==\\Absent\\,]\n\nLHH<-data[data$Light==\\Present\\,]\nLHH <- LHH[LHH$Flow.rate==\\High Flow\\,]\nLHH <- LHH[LHH$Chlorophyll==\\High Chlorophyll\\,]\nLHH <- LHH[LHH$Guano==\\Absent\\,]\n\nLHG<-data[data$Light==\\Present\\,]\nLHG <- LHG[LHG$Flow.rate==\\High Flow\\,]\nLHG <- LHG[LHG$Chlorophyll==\\No Chlorophyll\\,]\nLHG <- LHG[LHG$Guano==\\Present\\,]\n################################################################\n\n\n###    Light On, Extreme Flow\n\n####################################################################\n\nLEN<-data[data$Light==\\Present\\,]\nLEN <- LEN[LEN$Flow.rate==\\Extreme Flow\\,]\nLEN <- LEN[LEN$Chlorophyll==\\No Chlorophyll\\,]\nLEN <- LEN[LEN$Guano==\\Absent\\,]\n\nLEL<-data[data$Light==\\Present\\,]\nLEL <- LEL[LEL$Flow.rate==\\Extreme Flow\\,]\nLEL <- LEL[LEL$Chlorophyll==\\Low Chlorophyll\\,]\nLEL <- LEL[LEL$Guano==\\Absent\\,]\n\nLEM<-data[data$Light==\\Present\\,]\nLEM <- LEM[LEM$Flow.rate==\\Extreme Flow\\,]\nLEM <- LEM[LEM$Chlorophyll==\\Medium Chlorophyll\\,]\nLEM <- LEM[LEM$Guano==\\Absent\\,]\n\nLEH<-data[data$Light==\\Present\\,]\nLEH <- LEH[LEH$Flow.rate==\\Extreme Flow\\,]\nLEH <- LEH[LEH$Chlorophyll==\\High Chlorophyll\\,]\nLEH <- LEH[LEH$Guano==\\Absent\\,]\n\nLEG<-data[data$Light==\\Present\\,]\nLEG <- LEG[LEG$Flow.rate==\\Extreme Flow\\,]\nLEG <- LEG[LEG$Chlorophyll==\\No Chlorophyll\\,]\nLEG <- LEG[LEG$Guano==\\Present\\,]\n################################################################\n\n#####################################################################\n\n   ##  Light Off, NO Flow\n\n####################################################################\n\nDNN<-data[data$Light==\\Absent\\,]\nDNN <- DNN[DNN$Flow.rate==\\No Flow\\,]\nDNN <- DNN[DNN$Chlorophyll==\\No Chlorophyll\\,]\nDNN <- DNN[DNN$Guano==\\Absent\\,]\n\nDNL<-data[data$Light==\\Absent\\,]\nDNL <- DNL[DNL$Flow.rate==\\No Flow\\,]\nDNL <- DNL[DNL$Chlorophyll==\\Low Chlorophyll\\,]\nDNL <- DNL[DNL$Guano==\\Absent\\,]\n\nDNM<-data[data$Light==\\Absent\\,]\nDNM <- DNM[DNM$Flow.rate==\\No Flow\\,]\nDNM <- DNM[DNM$Chlorophyll==\\Medium Chlorophyll\\,]\nDNM <- DNM[DNM$Guano==\\Absent\\,]\n\nDNH <-data[data$Light==\\Absent\\,]\nDNH <- DNH[DNH$Flow.rate==\\No Flow\\,]\nDNH <- DNH[DNH$Chlorophyll==\\High Chlorophyll\\,]\nDNH <- DNH[DNH$Guano==\\Absent\\,]\n\nDNG<-data[data$Light==\\Absent\\,]\nDNG <- DNG[DNG$Flow.rate==\\No Flow\\,]\nDNG <- DNG[DNG$Chlorophyll==\\No Chlorophyll\\,]\nDNG <- DNG[DNG$Guano==\\Present\\,]\n##############################################################\n\n##    Lights Off, Low Flow\n\n###############################################################\n\nDLN <-data[data$Light==\\Absent\\,]\nDLN <- DLN[DLN$Flow.rate==\\Low Flow\\,]\nDLN <- DLN[DLN$Chlorophyll==\\No Chlorophyll\\,]\nDLN <- DLN[DLN$Guano==\\Absent\\,]\n\nDLL <-data[data$Light==\\Absent\\,]\nDLL <- DLL[DLL$Flow.rate==\\Low Flow\\,]\nDLL <- DLL[DLL$Chlorophyll==\\Low Chlorophyll\\,]\nDLL <- DLL[DLL$Guano==\\Absent\\,]\n\nDLM <-data[data$Light==\\Absent\\,]\nDLM <- DLM[DLM$Flow.rate==\\Low Flow\\,]\nDLM <- DLM[DLM$Chlorophyll==\\Medium Chlorophyll\\,]\nDLM <- DLM[DLM$Guano==\\Absent\\,]\n\nDLH <-data[data$Light==\\Absent\\,]\nDLH <- DLH[DLH$Flow.rate==\\Low Flow\\,]\nDLH <- DLH[DLH$Chlorophyll==\\High Chlorophyll\\,]\nDLH <- DLH[DLH$Guano==\\Absent\\,]\n\nDLG <-data[data$Light==\\Absent\\,]\nDLG <- DLG[DLG$Flow.rate==\\Low Flow\\,]\nDLG <- DLG[DLG$Chlorophyll==\\No Chlorophyll\\,]\nDLG <- DLG[DLG$Guano==\\Present\\,]\n#####################################################################\n\n##    Light Off, Medium Flow\n\n######################################################################\n\nDMN <-data[data$Light==\\Absent\\,]\nDMN <- DMN[DMN$Flow.rate==\\Medium Flow\\,]\nDMN <- DMN[DMN$Chlorophyll==\\No Chlorophyll\\,]\nDMN <- DMN[DMN$Guano==\\Absent\\,]\n\nDML <-data[data$Light==\\Absent\\,]\nDML <- DML[DML$Flow.rate==\\Medium Flow\\,]\nDML <- DML[DML$Chlorophyll==\\Low Chlorophyll\\,]\nDML <- DML[DML$Guano==\\Absent\\,]\n\nDMM <-data[data$Light==\\Absent\\,]\nDMM <- DMM[DMM$Flow.rate==\\Medium Flow\\,]\nDMM <- DMM[DMM$Chlorophyll==\\Medium Chlorophyll\\,]\nDMM <- DMM[DMM$Guano==\\Absent\\,]\n\nDMH <-data[data$Light==\\Absent\\,]\nDMH <- DMH[DMH$Flow.rate==\\Medium Flow\\,]\nDMH <- DMH[DMH$Chlorophyll==\\High Chlorophyll\\,]\nDMH <- DMH[DMH$Guano==\\Absent\\,]\n\nDMG <-data[data$Light==\\Absent\\,]\nDMG <- DMG[DMG$Flow.rate==\\Medium Flow\\,]\nDMG <- DMG[DMG$Chlorophyll==\\No Chlorophyll\\,]\nDMG <- DMG[DMG$Guano==\\Present\\,]\n################################################################\n\n###    Light Off, High Flow\n\n####################################################################\n\nDHN<-data[data$Light==\\Absent\\,]\nDHN <- DHN[DHN$Flow.rate==\\High Flow\\,]\nDHN <- DHN[DHN$Chlorophyll==\\No Chlorophyll\\,]\nDHN <- DHN[DHN$Guano==\\Absent\\,]\n\nDHL<-data[data$Light==\\Absent\\,]\nDHL <- DHL[DHL$Flow.rate==\\High Flow\\,]\nDHL <- DHL[DHL$Chlorophyll==\\Low Chlorophyll\\,]\nDHL <- DHL[DHL$Guano==\\Absent\\,]\n\nDHM<-data[data$Light==\\Absent\\,]\nDHM <- DHM[DHM$Flow.rate==\\High Flow\\,]\nDHM <- DHM[DHM$Chlorophyll==\\Medium Chlorophyll\\,]\nDHM <- DHM[DHM$Guano==\\Absent\\,]\n\nDHH<-data[data$Light==\\Absent\\,]\nDHH <- DHH[DHH$Flow.rate==\\High Flow\\,]\nDHH <- DHH[DHH$Chlorophyll==\\High Chlorophyll\\,]\nDHH <- DHH[DHH$Guano==\\Absent\\,]\n\nDHG<-data[data$Light==\\Absent\\,]\nDHG <- DHG[DHG$Flow.rate==\\High Flow\\,]\nDHG <- DHG[DHG$Chlorophyll==\\No Chlorophyll\\,]\nDHG <- DHG[DHG$Guano==\\Present\\,]\n################################################################\n\n\n###    Light Off, Extreme Flow\n\n####################################################################\n\nDEN<-data[data$Light==\\Absent\\,]\nDEN <- DEN[DEN$Flow.rate==\\Extreme Flow\\,]\nDEN <- DEN[DEN$Chlorophyll==\\No Chlorophyll\\,]\nDEN <- DEN[DEN$Guano==\\Absent\\,]\n\nDEL<-data[data$Light==\\Absent\\,]\nDEL <- DEL[DEL$Flow.rate==\\Extreme Flow\\,]\nDEL <- DEL[DEL$Chlorophyll==\\Low Chlorophyll\\,]\nDEL <- DEL[DEL$Guano==\\Absent\\,]\n\nDEM<-data[data$Light==\\Absent\\,]\nDEM <- DEM[DEM$Flow.rate==\\Extreme Flow\\,]\nDEM <- DEM[DEM$Chlorophyll==\\Medium Chlorophyll\\,]\nDEM <- DEM[DEM$Guano==\\Absent\\,]\n\nDEH<-data[data$Light==\\Absent\\,]\nDEH <- DEH[DEH$Flow.rate==\\Extreme Flow\\,]\nDEH <- DEH[DEH$Chlorophyll==\\High Chlorophyll\\,]\nDEH <- DEH[DEH$Guano==\\Absent\\,]\n\nDEG<-data[data$Light==\\Absent\\,]\nDEG <- DEG[DEG$Flow.rate==\\Extreme Flow\\,]\nDEG <- DEG[DEG$Chlorophyll==\\No Chlorophyll\\,]\nDEG <- DEG[DEG$Guano==\\Present\\,]\n\n##########################################################################\n\n##############################################################################\n ### setting data as circular\n##   Lights on\n\nhead(LNN)\n\nLNN <- circular(LNN$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #assign LNN subset to \\LNN\\ variable\nLNL <- circular(LNL$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #LNL\nLNM <- circular(LNM$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #LNM\nLNH <- circular(LNH$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #LNH\nLNG <- circular(LNG$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #LNG\n\nLLN <- circular(LLN$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #LLN\nLLL <- circular(LLL$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #LLL\nLLM <- circular(LLM$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #LLM\nLLH <- circular(LLH$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #LLH\nLLG <- circular(LLG$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #LLG\n\nLMN <- circular(LMN$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #LMN\nLML <- circular(LML$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #LML\nLMM <- circular(LMM$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #LMM\nLMH <- circular(LMH$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #LMH\nLMG <- circular(LMG$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #LMG\n\nLHN <- circular(LHN$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #LHN\nLHL <- circular(LHL$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #LHL\nLHM <- circular(LHM$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #LHM\nLHH <- circular(LHH$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #LHH\nLHG <- circular(LHG$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #LHG\n\nLEN <- circular(LEN$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #LEN\nLEL <- circular(LEL$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #LEL\nLEM <- circular(LEM$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #LEM\nLEH <- circular(LEH$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #LEH\nLEG <- circular(LEG$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #LEG\n\n############  Lights Off\n\nDNN <- circular(DNN$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #DNN\nDNL <- circular(DNL$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #DNL\nDNM <- circular(DNM$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #DNM\nDNH <- circular(DNH$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #DNH\nDNG <- circular(DNG$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #DNG\n\nDLN <- circular(DLN$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #DLN\nDLL <- circular(DLL$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #DLL\nDLM <- circular(DLM$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #DLM\nDLH <- circular(DLH$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #DLH\nDLG <- circular(DLG$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #DLG\n\nDMN <- circular(DMN$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #DMN\nDML <- circular(DML$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #DML\nDMM <- circular(DMM$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #DMM\nDMH <- circular(DMH$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #DMH\nDMG <- circular(DMG$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #DMG\n\nDHN <- circular(DHN$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #DHN\nDHL <- circular(DHL$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #DHL\nDHM <- circular(DHM$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #DHM\nDHH <- circular(DHH$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #DHH\nDHG <- circular(DHG$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #DHG\n\nDEN <- circular(DEN$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #DEN\nDEL <- circular(DEL$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #DEL\nDEM <- circular(DEM$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #DEM\nDEH <- circular(DEH$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #DEH\nDEG <- circular(DEG$pitch.perfect, units=\\degrees\\, template=\\geographics\\) #DEG\n######################################################################################\n\n############################################################################\nmean(LNN, na.rm = TRUE) #remove NAs from dataset, then find mean \nLNN.mean <- mean(LNN, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LNN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)  ### larger stack number = more zoomed out\narrows.circular(LNN.mean) #add mean to plot\n###########################################################################\n\nmean(LNL, na.rm = TRUE) #remove NAs from dataset, then find mean \nLNL.mean <- mean(LNL, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LNL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LNL.mean) #add mean to plot\n###########################################################################\n\nmean(LNM, na.rm = TRUE) #remove NAs from dataset, then find mean \nLNM.mean <- mean(LNM, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LNM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LNM.mean) #add mean to plot\n###########################################################################\n\nmean(LNH, na.rm = TRUE) #remove NAs from dataset, then find mean \nLNH.mean <- mean(LNH, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LNH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LNH.mean) #add mean to plot\n###########################################################################\n\nmean(LNG, na.rm = TRUE) #remove NAs from dataset, then find mean \nLNG.mean <- mean(LNG, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LNG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LNG.mean) #add mean to plot\n###########################################################################\n\n#########################################################################\n      ## Lights On Low Flow\n\n############################################################################\nmean(LLN, na.rm = TRUE) #remove NAs from dataset, then find mean \nLLN.mean <- mean(LLN, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LLN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LLN.mean) #add mean to plot\n###########################################################################\n\nmean(LLL, na.rm = TRUE) #remove NAs from dataset, then find mean \nLLL.mean <- mean(LLL, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LLL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LLL.mean) #add mean to plot\n###########################################################################\n\nmean(LLM, na.rm = TRUE) #remove NAs from dataset, then find mean \nLLM.mean <- mean(LLM, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LLM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LLM.mean) #add mean to plot\n###########################################################################\n\nmean(LLH, na.rm = TRUE) #remove NAs from dataset, then find mean \nLLH.mean <- mean(LLH, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LLH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LLH.mean) #add mean to plot\n###########################################################################\n\nmean(LLG, na.rm = TRUE) #remove NAs from dataset, then find mean \nLLG.mean <- mean(LLG, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LLG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LLG.mean) #add mean to plot\n###########################################################################\n\n\n#########################################################################\n      ## Lights On Medium Flow\n\n############################################################################\nmean(LMN, na.rm = TRUE) #remove NAs from dataset, then find mean \nLMN.mean <- mean(LMN, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LMN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LMN.mean) #add mean to plot\n###########################################################################\n\nmean(LML, na.rm = TRUE) #remove NAs from dataset, then find mean \nLML.mean <- mean(LML, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LML, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LML.mean) #add mean to plot\n###########################################################################\n\nmean(LMM, na.rm = TRUE) #remove NAs from dataset, then find mean \nLMM.mean <- mean(LMM, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LMM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LMM.mean) #add mean to plot\n###########################################################################\n\nmean(LMH, na.rm = TRUE) #remove NAs from dataset, then find mean \nLMH.mean <- mean(LMH, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LMH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LMH.mean) #add mean to plot\n###########################################################################\n\nmean(LMG, na.rm = TRUE) #remove NAs from dataset, then find mean \nLMG.mean <- mean(LMG, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LMG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LMG.mean) #add mean to plot\n###########################################################################\n\n\n#########################################################################\n      ## Lights On High Flow\n\n############################################################################\nmean(LHN, na.rm = TRUE) #remove NAs from dataset, then find mean \nLHN.mean <- mean(LHN, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LHN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LHN.mean) #add mean to plot\n###########################################################################\n\nmean(LHL, na.rm = TRUE) #remove NAs from dataset, then find mean \nLHL.mean <- mean(LHL, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LHL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LHL.mean) #add mean to plot\n###########################################################################\n\nmean(LHM, na.rm = TRUE) #remove NAs from dataset, then find mean \nLHM.mean <- mean(LHM, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LHM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LHM.mean) #add mean to plot\n###########################################################################\n\nmean(LHH, na.rm = TRUE) #remove NAs from dataset, then find mean \nLHH.mean <- mean(LHH, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LHH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LHH.mean) #add mean to plot\n###########################################################################\n\nmean(LHG, na.rm = TRUE) #remove NAs from dataset, then find mean \nLHG.mean <- mean(LHG, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LHG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LHG.mean) #add mean to plot\n###########################################################################\n\n\n\n#########################################################################\n      ## Lights On Extreme Flow\n\n############################################################################\nmean(LEN, na.rm = TRUE) #remove NAs from dataset, then find mean \nLEN.mean <- mean(LEN, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LEN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LEN.mean) #add mean to plot\n###########################################################################\n\nmean(LEL, na.rm = TRUE) #remove NAs from dataset, then find mean \nLEL.mean <- mean(LEL, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LEL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LEL.mean) #add mean to plot\n###########################################################################\n\nmean(LEM, na.rm = TRUE) #remove NAs from dataset, then find mean \nLEM.mean <- mean(LEM, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LEM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LEM.mean) #add mean to plot\n###########################################################################\n\nmean(LEH, na.rm = TRUE) #remove NAs from dataset, then find mean \nLEH.mean <- mean(LEH, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LEH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LEH.mean) #add mean to plot\n###########################################################################\n\nmean(LEG, na.rm = TRUE) #remove NAs from dataset, then find mean \nLEG.mean <- mean(LEG, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(LEG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(LEG.mean) #add mean to plot\n###########################################################################\n\n#########################################################################\n      ## Lights Off No Flow\n\n############################################################################\nmean(DNN, na.rm = TRUE) #remove NAs from dataset, then find mean \nDNN.mean <- mean(DNN, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DNN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)  ### larger stack number = more zoomed out\narrows.circular(DNN.mean) #add mean to plot\n###########################################################################\n\nmean(DNL, na.rm = TRUE) #remove NAs from dataset, then find mean \nDNL.mean <- mean(DNL, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DNL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DNL.mean) #add mean to plot\n###########################################################################\n\nmean(DNM, na.rm = TRUE) #remove NAs from dataset, then find mean \nDNM.mean <- mean(DNM, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DNM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DNM.mean) #add mean to plot\n###########################################################################\n\nmean(DNH, na.rm = TRUE) #remove NAs from dataset, then find mean \nDNH.mean <- mean(DNH, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DNH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DNH.mean) #add mean to plot\n###########################################################################\n\nmean(DNG, na.rm = TRUE) #remove NAs from dataset, then find mean \nDNG.mean <- mean(DNG, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DNG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DNG.mean) #add mean to plot\n###########################################################################\n\n#########################################################################\n      ## Lights Off Low Flow\n\n############################################################################\nmean(DLN, na.rm = TRUE) #remove NAs from dataset, then find mean \nDLN.mean <- mean(DLN, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DLN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DLN.mean) #add mean to plot\n###########################################################################\n\nmean(DLL, na.rm = TRUE) #remove NAs from dataset, then find mean \nDLL.mean <- mean(DLL, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DLL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DLL.mean) #add mean to plot\n###########################################################################\n\nmean(DLM, na.rm = TRUE) #remove NAs from dataset, then find mean \nDLM.mean <- mean(DLM, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DLM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DLM.mean) #add mean to plot\n###########################################################################\n\nmean(DLH, na.rm = TRUE) #remove NAs from dataset, then find mean \nDLH.mean <- mean(DLH, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DLH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DLH.mean) #add mean to plot\n###########################################################################\n\nmean(DLG, na.rm = TRUE) #remove NAs from dataset, then find mean \nDLG.mean <- mean(DLG, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DLG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DLG.mean) #add mean to plot\n###########################################################################\n\n\n#########################################################################\n      ## Lights On Medium Flow\n\n############################################################################\nmean(DMN, na.rm = TRUE) #remove NAs from dataset, then find mean \nDMN.mean <- mean(DMN, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DMN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DMN.mean) #add mean to plot\n###########################################################################\n\nmean(DML, na.rm = TRUE) #remove NAs from dataset, then find mean \nDML.mean <- mean(DML, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DML, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DML.mean) #add mean to plot\n###########################################################################\n\nmean(DMM, na.rm = TRUE) #remove NAs from dataset, then find mean \nDMM.mean <- mean(DMM, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DMM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DMM.mean) #add mean to plot\n###########################################################################\n\nmean(DMH, na.rm = TRUE) #remove NAs from dataset, then find mean \nDMH.mean <- mean(DMH, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DMH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DMH.mean) #add mean to plot\n###########################################################################\n\nmean(DMG, na.rm = TRUE) #remove NAs from dataset, then find mean \nDMG.mean <- mean(DMG, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DMG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DMG.mean) #add mean to plot\n###########################################################################\n\n\n#########################################################################\n      ## Lights Off High Flow\n\n############################################################################\nmean(DHN, na.rm = TRUE) #remove NAs from dataset, then find mean \nDHN.mean <- mean(DHN, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DHN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DHN.mean) #add mean to plot\n###########################################################################\n\nmean(DHL, na.rm = TRUE) #remove NAs from dataset, then find mean \nDHL.mean <- mean(DHL, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DHL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DHL.mean) #add mean to plot\n###########################################################################\n\nmean(DHM, na.rm = TRUE) #remove NAs from dataset, then find mean \nDHM.mean <- mean(DHM, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DHM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DHM.mean) #add mean to plot\n###########################################################################\n\nmean(DHH, na.rm = TRUE) #remove NAs from dataset, then find mean \nDHH.mean <- mean(DHH, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DHH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DHH.mean) #add mean to plot\n###########################################################################\n\nmean(DHG, na.rm = TRUE) #remove NAs from dataset, then find mean \nDHG.mean <- mean(DHG, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DHG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DHG.mean) #add mean to plot\n###########################################################################\n\n#########################################################################\n      ## Lights On Extreme Flow\n\n############################################################################\nmean(DEN, na.rm = TRUE) #remove NAs from dataset, then find mean \nDEN.mean <- mean(DEN, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DEN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DEN.mean) #add mean to plot\n###########################################################################\n\nmean(DEL, na.rm = TRUE) #remove NAs from dataset, then find mean \nDEL.mean <- mean(DEL, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DEL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DEL.mean) #add mean to plot\n###########################################################################\n\nmean(DEM, na.rm = TRUE) #remove NAs from dataset, then find mean \nDEM.mean <- mean(DEM, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DEM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DEM.mean) #add mean to plot\n###########################################################################\n\nmean(DEH, na.rm = TRUE) #remove NAs from dataset, then find mean \nDEH.mean <- mean(DEH, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DEH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)\narrows.circular(DEH.mean) #add mean to plot\n###########################################################################\n\nmean(DEG, na.rm = TRUE) #remove NAs from dataset, then find mean \nDEG.mean <- mean(DEG, na.rm = TRUE) #assigns to the 'LNN.mean' object\n\nplot.circular(DEG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)  ### larger stack number = more zoomed out\narrows.circular(DEG.mean) #add mean to plot\n###########################################################################\n\n\n```\n```"} -->

```r
```r
LNN<-data[data$Light==\Present\,]
LNN <- LNN[LNN$Flow.rate==\No Flow\,]
LNN <- LNN[LNN$Chlorophyll==\No Chlorophyll\,]
LNN <- LNN[LNN$Guano==\Absent\,]

LNL<-data[data$Light==\Present\,]
LNL <- LNL[LNL$Flow.rate==\No Flow\,]
LNL <- LNL[LNL$Chlorophyll==\Low Chlorophyll\,]
LNL <- LNL[LNL$Guano==\Absent\,]

LNM<-data[data$Light==\Present\,]
LNM <- LNM[LNM$Flow.rate==\No Flow\,]
LNM <- LNM[LNM$Chlorophyll==\Medium Chlorophyll\,]
LNM <- LNM[LNM$Guano==\Absent\,]

LNH<-data[data$Light==\Present\,]
LNH <- LNH[LNH$Flow.rate==\No Flow\,]
LNH <- LNH[LNH$Chlorophyll==\High Chlorophyll\,]
LNH <- LNH[LNH$Guano==\Absent\,]

LNG<-data[data$Light==\Present\,]
LNG <- LNG[LNG$Flow.rate==\No Flow\,]
LNG <- LNG[LNG$Chlorophyll==\No Chlorophyll\,]
LNG <- LNG[LNG$Guano==\Present\,]
##############################################################

##    Lights On, Low Flow

###############################################################

LLN<-data[data$Light==\Present\,]
LLN <- LLN[LLN$Flow.rate==\Low Flow\,]
LLN <- LLN[LLN$Chlorophyll==\No Chlorophyll\,]
LLN <- LLN[LLN$Guano==\Absent\,]

LLL<-data[data$Light==\Present\,]
LLL <- LLL[LLL$Flow.rate==\Low Flow\,]
LLL <- LLL[LLL$Chlorophyll==\Low Chlorophyll\,]
LLL <- LLL[LLL$Guano==\Absent\,]

LLM<-data[data$Light==\Present\,]
LLM <- LLM[LLM$Flow.rate==\Low Flow\,]
LLM <- LLM[LLM$Chlorophyll==\Medium Chlorophyll\,]
LLM <- LLM[LLM$Guano==\Absent\,]

LLH<-data[data$Light==\Present\,]
LLH <- LLH[LLH$Flow.rate==\Low Flow\,]
LLH <- LLH[LLH$Chlorophyll==\High Chlorophyll\,]
LLH <- LLH[LLH$Guano==\Absent\,]

LLG<-data[data$Light==\Present\,]
LLG <- LLG[LLG$Flow.rate==\Low Flow\,]
LLG <- LLG[LLG$Chlorophyll==\No Chlorophyll\,]
LLG <- LLG[LLG$Guano==\Present\,]
#####################################################################

##    Light On, Medium Flow

######################################################################

LMN<-data[data$Light==\Present\,]
LMN <- LMN[LMN$Flow.rate==\Medium Flow\,]
LMN <- LMN[LMN$Chlorophyll==\No Chlorophyll\,]
LMN <- LMN[LMN$Guano==\Absent\,]

LML<-data[data$Light==\Present\,]
LML <- LML[LML$Flow.rate==\Medium Flow\,]
LML <- LML[LML$Chlorophyll==\Low Chlorophyll\,]
LML <- LML[LML$Guano==\Absent\,]

LMM<-data[data$Light==\Present\,]
LMM <- LMM[LMM$Flow.rate==\Medium Flow\,]
LMM <- LMM[LMM$Chlorophyll==\Medium Chlorophyll\,]
LMM <- LMM[LMM$Guano==\Absent\,]

LMH<-data[data$Light==\Present\,]
LMH <- LMH[LMH$Flow.rate==\Medium Flow\,]
LMH <- LMH[LMH$Chlorophyll==\High Chlorophyll\,]
LMH <- LMH[LMH$Guano==\Absent\,]

LMG<-data[data$Light==\Present\,]
LMG <- LMG[LMG$Flow.rate==\Medium Flow\,]
LMG <- LMG[LMG$Chlorophyll==\No Chlorophyll\,]
LMG <- LMG[LMG$Guano==\Present\,]
################################################################

###    Light On, High Flow

####################################################################

LHN<-data[data$Light==\Present\,]
LHN <- LHN[LHN$Flow.rate==\High Flow\,]
LHN <- LHN[LHN$Chlorophyll==\No Chlorophyll\,]
LHN <- LHN[LHN$Guano==\Absent\,]

LHL<-data[data$Light==\Present\,]
LHL <- LHL[LHL$Flow.rate==\High Flow\,]
LHL <- LHL[LHL$Chlorophyll==\Low Chlorophyll\,]
LHL <- LHL[LHL$Guano==\Absent\,]

LHM<-data[data$Light==\Present\,]
LHM <- LHM[LHM$Flow.rate==\High Flow\,]
LHM <- LHM[LHM$Chlorophyll==\Medium Chlorophyll\,]
LHM <- LHM[LHM$Guano==\Absent\,]

LHH<-data[data$Light==\Present\,]
LHH <- LHH[LHH$Flow.rate==\High Flow\,]
LHH <- LHH[LHH$Chlorophyll==\High Chlorophyll\,]
LHH <- LHH[LHH$Guano==\Absent\,]

LHG<-data[data$Light==\Present\,]
LHG <- LHG[LHG$Flow.rate==\High Flow\,]
LHG <- LHG[LHG$Chlorophyll==\No Chlorophyll\,]
LHG <- LHG[LHG$Guano==\Present\,]
################################################################


###    Light On, Extreme Flow

####################################################################

LEN<-data[data$Light==\Present\,]
LEN <- LEN[LEN$Flow.rate==\Extreme Flow\,]
LEN <- LEN[LEN$Chlorophyll==\No Chlorophyll\,]
LEN <- LEN[LEN$Guano==\Absent\,]

LEL<-data[data$Light==\Present\,]
LEL <- LEL[LEL$Flow.rate==\Extreme Flow\,]
LEL <- LEL[LEL$Chlorophyll==\Low Chlorophyll\,]
LEL <- LEL[LEL$Guano==\Absent\,]

LEM<-data[data$Light==\Present\,]
LEM <- LEM[LEM$Flow.rate==\Extreme Flow\,]
LEM <- LEM[LEM$Chlorophyll==\Medium Chlorophyll\,]
LEM <- LEM[LEM$Guano==\Absent\,]

LEH<-data[data$Light==\Present\,]
LEH <- LEH[LEH$Flow.rate==\Extreme Flow\,]
LEH <- LEH[LEH$Chlorophyll==\High Chlorophyll\,]
LEH <- LEH[LEH$Guano==\Absent\,]

LEG<-data[data$Light==\Present\,]
LEG <- LEG[LEG$Flow.rate==\Extreme Flow\,]
LEG <- LEG[LEG$Chlorophyll==\No Chlorophyll\,]
LEG <- LEG[LEG$Guano==\Present\,]
################################################################

#####################################################################

   ##  Light Off, NO Flow

####################################################################

DNN<-data[data$Light==\Absent\,]
DNN <- DNN[DNN$Flow.rate==\No Flow\,]
DNN <- DNN[DNN$Chlorophyll==\No Chlorophyll\,]
DNN <- DNN[DNN$Guano==\Absent\,]

DNL<-data[data$Light==\Absent\,]
DNL <- DNL[DNL$Flow.rate==\No Flow\,]
DNL <- DNL[DNL$Chlorophyll==\Low Chlorophyll\,]
DNL <- DNL[DNL$Guano==\Absent\,]

DNM<-data[data$Light==\Absent\,]
DNM <- DNM[DNM$Flow.rate==\No Flow\,]
DNM <- DNM[DNM$Chlorophyll==\Medium Chlorophyll\,]
DNM <- DNM[DNM$Guano==\Absent\,]

DNH <-data[data$Light==\Absent\,]
DNH <- DNH[DNH$Flow.rate==\No Flow\,]
DNH <- DNH[DNH$Chlorophyll==\High Chlorophyll\,]
DNH <- DNH[DNH$Guano==\Absent\,]

DNG<-data[data$Light==\Absent\,]
DNG <- DNG[DNG$Flow.rate==\No Flow\,]
DNG <- DNG[DNG$Chlorophyll==\No Chlorophyll\,]
DNG <- DNG[DNG$Guano==\Present\,]
##############################################################

##    Lights Off, Low Flow

###############################################################

DLN <-data[data$Light==\Absent\,]
DLN <- DLN[DLN$Flow.rate==\Low Flow\,]
DLN <- DLN[DLN$Chlorophyll==\No Chlorophyll\,]
DLN <- DLN[DLN$Guano==\Absent\,]

DLL <-data[data$Light==\Absent\,]
DLL <- DLL[DLL$Flow.rate==\Low Flow\,]
DLL <- DLL[DLL$Chlorophyll==\Low Chlorophyll\,]
DLL <- DLL[DLL$Guano==\Absent\,]

DLM <-data[data$Light==\Absent\,]
DLM <- DLM[DLM$Flow.rate==\Low Flow\,]
DLM <- DLM[DLM$Chlorophyll==\Medium Chlorophyll\,]
DLM <- DLM[DLM$Guano==\Absent\,]

DLH <-data[data$Light==\Absent\,]
DLH <- DLH[DLH$Flow.rate==\Low Flow\,]
DLH <- DLH[DLH$Chlorophyll==\High Chlorophyll\,]
DLH <- DLH[DLH$Guano==\Absent\,]

DLG <-data[data$Light==\Absent\,]
DLG <- DLG[DLG$Flow.rate==\Low Flow\,]
DLG <- DLG[DLG$Chlorophyll==\No Chlorophyll\,]
DLG <- DLG[DLG$Guano==\Present\,]
#####################################################################

##    Light Off, Medium Flow

######################################################################

DMN <-data[data$Light==\Absent\,]
DMN <- DMN[DMN$Flow.rate==\Medium Flow\,]
DMN <- DMN[DMN$Chlorophyll==\No Chlorophyll\,]
DMN <- DMN[DMN$Guano==\Absent\,]

DML <-data[data$Light==\Absent\,]
DML <- DML[DML$Flow.rate==\Medium Flow\,]
DML <- DML[DML$Chlorophyll==\Low Chlorophyll\,]
DML <- DML[DML$Guano==\Absent\,]

DMM <-data[data$Light==\Absent\,]
DMM <- DMM[DMM$Flow.rate==\Medium Flow\,]
DMM <- DMM[DMM$Chlorophyll==\Medium Chlorophyll\,]
DMM <- DMM[DMM$Guano==\Absent\,]

DMH <-data[data$Light==\Absent\,]
DMH <- DMH[DMH$Flow.rate==\Medium Flow\,]
DMH <- DMH[DMH$Chlorophyll==\High Chlorophyll\,]
DMH <- DMH[DMH$Guano==\Absent\,]

DMG <-data[data$Light==\Absent\,]
DMG <- DMG[DMG$Flow.rate==\Medium Flow\,]
DMG <- DMG[DMG$Chlorophyll==\No Chlorophyll\,]
DMG <- DMG[DMG$Guano==\Present\,]
################################################################

###    Light Off, High Flow

####################################################################

DHN<-data[data$Light==\Absent\,]
DHN <- DHN[DHN$Flow.rate==\High Flow\,]
DHN <- DHN[DHN$Chlorophyll==\No Chlorophyll\,]
DHN <- DHN[DHN$Guano==\Absent\,]

DHL<-data[data$Light==\Absent\,]
DHL <- DHL[DHL$Flow.rate==\High Flow\,]
DHL <- DHL[DHL$Chlorophyll==\Low Chlorophyll\,]
DHL <- DHL[DHL$Guano==\Absent\,]

DHM<-data[data$Light==\Absent\,]
DHM <- DHM[DHM$Flow.rate==\High Flow\,]
DHM <- DHM[DHM$Chlorophyll==\Medium Chlorophyll\,]
DHM <- DHM[DHM$Guano==\Absent\,]

DHH<-data[data$Light==\Absent\,]
DHH <- DHH[DHH$Flow.rate==\High Flow\,]
DHH <- DHH[DHH$Chlorophyll==\High Chlorophyll\,]
DHH <- DHH[DHH$Guano==\Absent\,]

DHG<-data[data$Light==\Absent\,]
DHG <- DHG[DHG$Flow.rate==\High Flow\,]
DHG <- DHG[DHG$Chlorophyll==\No Chlorophyll\,]
DHG <- DHG[DHG$Guano==\Present\,]
################################################################


###    Light Off, Extreme Flow

####################################################################

DEN<-data[data$Light==\Absent\,]
DEN <- DEN[DEN$Flow.rate==\Extreme Flow\,]
DEN <- DEN[DEN$Chlorophyll==\No Chlorophyll\,]
DEN <- DEN[DEN$Guano==\Absent\,]

DEL<-data[data$Light==\Absent\,]
DEL <- DEL[DEL$Flow.rate==\Extreme Flow\,]
DEL <- DEL[DEL$Chlorophyll==\Low Chlorophyll\,]
DEL <- DEL[DEL$Guano==\Absent\,]

DEM<-data[data$Light==\Absent\,]
DEM <- DEM[DEM$Flow.rate==\Extreme Flow\,]
DEM <- DEM[DEM$Chlorophyll==\Medium Chlorophyll\,]
DEM <- DEM[DEM$Guano==\Absent\,]

DEH<-data[data$Light==\Absent\,]
DEH <- DEH[DEH$Flow.rate==\Extreme Flow\,]
DEH <- DEH[DEH$Chlorophyll==\High Chlorophyll\,]
DEH <- DEH[DEH$Guano==\Absent\,]

DEG<-data[data$Light==\Absent\,]
DEG <- DEG[DEG$Flow.rate==\Extreme Flow\,]
DEG <- DEG[DEG$Chlorophyll==\No Chlorophyll\,]
DEG <- DEG[DEG$Guano==\Present\,]

##########################################################################

##############################################################################
 ### setting data as circular
##   Lights on

head(LNN)

LNN <- circular(LNN$pitch.perfect, units=\degrees\, template=\geographics\) #assign LNN subset to \LNN\ variable
LNL <- circular(LNL$pitch.perfect, units=\degrees\, template=\geographics\) #LNL
LNM <- circular(LNM$pitch.perfect, units=\degrees\, template=\geographics\) #LNM
LNH <- circular(LNH$pitch.perfect, units=\degrees\, template=\geographics\) #LNH
LNG <- circular(LNG$pitch.perfect, units=\degrees\, template=\geographics\) #LNG

LLN <- circular(LLN$pitch.perfect, units=\degrees\, template=\geographics\) #LLN
LLL <- circular(LLL$pitch.perfect, units=\degrees\, template=\geographics\) #LLL
LLM <- circular(LLM$pitch.perfect, units=\degrees\, template=\geographics\) #LLM
LLH <- circular(LLH$pitch.perfect, units=\degrees\, template=\geographics\) #LLH
LLG <- circular(LLG$pitch.perfect, units=\degrees\, template=\geographics\) #LLG

LMN <- circular(LMN$pitch.perfect, units=\degrees\, template=\geographics\) #LMN
LML <- circular(LML$pitch.perfect, units=\degrees\, template=\geographics\) #LML
LMM <- circular(LMM$pitch.perfect, units=\degrees\, template=\geographics\) #LMM
LMH <- circular(LMH$pitch.perfect, units=\degrees\, template=\geographics\) #LMH
LMG <- circular(LMG$pitch.perfect, units=\degrees\, template=\geographics\) #LMG

LHN <- circular(LHN$pitch.perfect, units=\degrees\, template=\geographics\) #LHN
LHL <- circular(LHL$pitch.perfect, units=\degrees\, template=\geographics\) #LHL
LHM <- circular(LHM$pitch.perfect, units=\degrees\, template=\geographics\) #LHM
LHH <- circular(LHH$pitch.perfect, units=\degrees\, template=\geographics\) #LHH
LHG <- circular(LHG$pitch.perfect, units=\degrees\, template=\geographics\) #LHG

LEN <- circular(LEN$pitch.perfect, units=\degrees\, template=\geographics\) #LEN
LEL <- circular(LEL$pitch.perfect, units=\degrees\, template=\geographics\) #LEL
LEM <- circular(LEM$pitch.perfect, units=\degrees\, template=\geographics\) #LEM
LEH <- circular(LEH$pitch.perfect, units=\degrees\, template=\geographics\) #LEH
LEG <- circular(LEG$pitch.perfect, units=\degrees\, template=\geographics\) #LEG

############  Lights Off

DNN <- circular(DNN$pitch.perfect, units=\degrees\, template=\geographics\) #DNN
DNL <- circular(DNL$pitch.perfect, units=\degrees\, template=\geographics\) #DNL
DNM <- circular(DNM$pitch.perfect, units=\degrees\, template=\geographics\) #DNM
DNH <- circular(DNH$pitch.perfect, units=\degrees\, template=\geographics\) #DNH
DNG <- circular(DNG$pitch.perfect, units=\degrees\, template=\geographics\) #DNG

DLN <- circular(DLN$pitch.perfect, units=\degrees\, template=\geographics\) #DLN
DLL <- circular(DLL$pitch.perfect, units=\degrees\, template=\geographics\) #DLL
DLM <- circular(DLM$pitch.perfect, units=\degrees\, template=\geographics\) #DLM
DLH <- circular(DLH$pitch.perfect, units=\degrees\, template=\geographics\) #DLH
DLG <- circular(DLG$pitch.perfect, units=\degrees\, template=\geographics\) #DLG

DMN <- circular(DMN$pitch.perfect, units=\degrees\, template=\geographics\) #DMN
DML <- circular(DML$pitch.perfect, units=\degrees\, template=\geographics\) #DML
DMM <- circular(DMM$pitch.perfect, units=\degrees\, template=\geographics\) #DMM
DMH <- circular(DMH$pitch.perfect, units=\degrees\, template=\geographics\) #DMH
DMG <- circular(DMG$pitch.perfect, units=\degrees\, template=\geographics\) #DMG

DHN <- circular(DHN$pitch.perfect, units=\degrees\, template=\geographics\) #DHN
DHL <- circular(DHL$pitch.perfect, units=\degrees\, template=\geographics\) #DHL
DHM <- circular(DHM$pitch.perfect, units=\degrees\, template=\geographics\) #DHM
DHH <- circular(DHH$pitch.perfect, units=\degrees\, template=\geographics\) #DHH
DHG <- circular(DHG$pitch.perfect, units=\degrees\, template=\geographics\) #DHG

DEN <- circular(DEN$pitch.perfect, units=\degrees\, template=\geographics\) #DEN
DEL <- circular(DEL$pitch.perfect, units=\degrees\, template=\geographics\) #DEL
DEM <- circular(DEM$pitch.perfect, units=\degrees\, template=\geographics\) #DEM
DEH <- circular(DEH$pitch.perfect, units=\degrees\, template=\geographics\) #DEH
DEG <- circular(DEG$pitch.perfect, units=\degrees\, template=\geographics\) #DEG
######################################################################################

############################################################################
mean(LNN, na.rm = TRUE) #remove NAs from dataset, then find mean 
LNN.mean <- mean(LNN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LNN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)  ### larger stack number = more zoomed out
arrows.circular(LNN.mean) #add mean to plot
###########################################################################

mean(LNL, na.rm = TRUE) #remove NAs from dataset, then find mean 
LNL.mean <- mean(LNL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LNL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LNL.mean) #add mean to plot
###########################################################################

mean(LNM, na.rm = TRUE) #remove NAs from dataset, then find mean 
LNM.mean <- mean(LNM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LNM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LNM.mean) #add mean to plot
###########################################################################

mean(LNH, na.rm = TRUE) #remove NAs from dataset, then find mean 
LNH.mean <- mean(LNH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LNH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LNH.mean) #add mean to plot
###########################################################################

mean(LNG, na.rm = TRUE) #remove NAs from dataset, then find mean 
LNG.mean <- mean(LNG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LNG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LNG.mean) #add mean to plot
###########################################################################

#########################################################################
      ## Lights On Low Flow

############################################################################
mean(LLN, na.rm = TRUE) #remove NAs from dataset, then find mean 
LLN.mean <- mean(LLN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LLN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LLN.mean) #add mean to plot
###########################################################################

mean(LLL, na.rm = TRUE) #remove NAs from dataset, then find mean 
LLL.mean <- mean(LLL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LLL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LLL.mean) #add mean to plot
###########################################################################

mean(LLM, na.rm = TRUE) #remove NAs from dataset, then find mean 
LLM.mean <- mean(LLM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LLM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LLM.mean) #add mean to plot
###########################################################################

mean(LLH, na.rm = TRUE) #remove NAs from dataset, then find mean 
LLH.mean <- mean(LLH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LLH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LLH.mean) #add mean to plot
###########################################################################

mean(LLG, na.rm = TRUE) #remove NAs from dataset, then find mean 
LLG.mean <- mean(LLG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LLG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LLG.mean) #add mean to plot
###########################################################################


#########################################################################
      ## Lights On Medium Flow

############################################################################
mean(LMN, na.rm = TRUE) #remove NAs from dataset, then find mean 
LMN.mean <- mean(LMN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LMN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LMN.mean) #add mean to plot
###########################################################################

mean(LML, na.rm = TRUE) #remove NAs from dataset, then find mean 
LML.mean <- mean(LML, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LML, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LML.mean) #add mean to plot
###########################################################################

mean(LMM, na.rm = TRUE) #remove NAs from dataset, then find mean 
LMM.mean <- mean(LMM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LMM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LMM.mean) #add mean to plot
###########################################################################

mean(LMH, na.rm = TRUE) #remove NAs from dataset, then find mean 
LMH.mean <- mean(LMH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LMH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LMH.mean) #add mean to plot
###########################################################################

mean(LMG, na.rm = TRUE) #remove NAs from dataset, then find mean 
LMG.mean <- mean(LMG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LMG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LMG.mean) #add mean to plot
###########################################################################


#########################################################################
      ## Lights On High Flow

############################################################################
mean(LHN, na.rm = TRUE) #remove NAs from dataset, then find mean 
LHN.mean <- mean(LHN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LHN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LHN.mean) #add mean to plot
###########################################################################

mean(LHL, na.rm = TRUE) #remove NAs from dataset, then find mean 
LHL.mean <- mean(LHL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LHL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LHL.mean) #add mean to plot
###########################################################################

mean(LHM, na.rm = TRUE) #remove NAs from dataset, then find mean 
LHM.mean <- mean(LHM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LHM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LHM.mean) #add mean to plot
###########################################################################

mean(LHH, na.rm = TRUE) #remove NAs from dataset, then find mean 
LHH.mean <- mean(LHH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LHH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LHH.mean) #add mean to plot
###########################################################################

mean(LHG, na.rm = TRUE) #remove NAs from dataset, then find mean 
LHG.mean <- mean(LHG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LHG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LHG.mean) #add mean to plot
###########################################################################



#########################################################################
      ## Lights On Extreme Flow

############################################################################
mean(LEN, na.rm = TRUE) #remove NAs from dataset, then find mean 
LEN.mean <- mean(LEN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LEN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LEN.mean) #add mean to plot
###########################################################################

mean(LEL, na.rm = TRUE) #remove NAs from dataset, then find mean 
LEL.mean <- mean(LEL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LEL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LEL.mean) #add mean to plot
###########################################################################

mean(LEM, na.rm = TRUE) #remove NAs from dataset, then find mean 
LEM.mean <- mean(LEM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LEM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LEM.mean) #add mean to plot
###########################################################################

mean(LEH, na.rm = TRUE) #remove NAs from dataset, then find mean 
LEH.mean <- mean(LEH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LEH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LEH.mean) #add mean to plot
###########################################################################

mean(LEG, na.rm = TRUE) #remove NAs from dataset, then find mean 
LEG.mean <- mean(LEG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LEG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LEG.mean) #add mean to plot
###########################################################################

#########################################################################
      ## Lights Off No Flow

############################################################################
mean(DNN, na.rm = TRUE) #remove NAs from dataset, then find mean 
DNN.mean <- mean(DNN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DNN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)  ### larger stack number = more zoomed out
arrows.circular(DNN.mean) #add mean to plot
###########################################################################

mean(DNL, na.rm = TRUE) #remove NAs from dataset, then find mean 
DNL.mean <- mean(DNL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DNL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DNL.mean) #add mean to plot
###########################################################################

mean(DNM, na.rm = TRUE) #remove NAs from dataset, then find mean 
DNM.mean <- mean(DNM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DNM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DNM.mean) #add mean to plot
###########################################################################

mean(DNH, na.rm = TRUE) #remove NAs from dataset, then find mean 
DNH.mean <- mean(DNH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DNH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DNH.mean) #add mean to plot
###########################################################################

mean(DNG, na.rm = TRUE) #remove NAs from dataset, then find mean 
DNG.mean <- mean(DNG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DNG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DNG.mean) #add mean to plot
###########################################################################

#########################################################################
      ## Lights Off Low Flow

############################################################################
mean(DLN, na.rm = TRUE) #remove NAs from dataset, then find mean 
DLN.mean <- mean(DLN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DLN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DLN.mean) #add mean to plot
###########################################################################

mean(DLL, na.rm = TRUE) #remove NAs from dataset, then find mean 
DLL.mean <- mean(DLL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DLL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DLL.mean) #add mean to plot
###########################################################################

mean(DLM, na.rm = TRUE) #remove NAs from dataset, then find mean 
DLM.mean <- mean(DLM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DLM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DLM.mean) #add mean to plot
###########################################################################

mean(DLH, na.rm = TRUE) #remove NAs from dataset, then find mean 
DLH.mean <- mean(DLH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DLH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DLH.mean) #add mean to plot
###########################################################################

mean(DLG, na.rm = TRUE) #remove NAs from dataset, then find mean 
DLG.mean <- mean(DLG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DLG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DLG.mean) #add mean to plot
###########################################################################


#########################################################################
      ## Lights On Medium Flow

############################################################################
mean(DMN, na.rm = TRUE) #remove NAs from dataset, then find mean 
DMN.mean <- mean(DMN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DMN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DMN.mean) #add mean to plot
###########################################################################

mean(DML, na.rm = TRUE) #remove NAs from dataset, then find mean 
DML.mean <- mean(DML, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DML, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DML.mean) #add mean to plot
###########################################################################

mean(DMM, na.rm = TRUE) #remove NAs from dataset, then find mean 
DMM.mean <- mean(DMM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DMM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DMM.mean) #add mean to plot
###########################################################################

mean(DMH, na.rm = TRUE) #remove NAs from dataset, then find mean 
DMH.mean <- mean(DMH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DMH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DMH.mean) #add mean to plot
###########################################################################

mean(DMG, na.rm = TRUE) #remove NAs from dataset, then find mean 
DMG.mean <- mean(DMG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DMG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DMG.mean) #add mean to plot
###########################################################################


#########################################################################
      ## Lights Off High Flow

############################################################################
mean(DHN, na.rm = TRUE) #remove NAs from dataset, then find mean 
DHN.mean <- mean(DHN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DHN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DHN.mean) #add mean to plot
###########################################################################

mean(DHL, na.rm = TRUE) #remove NAs from dataset, then find mean 
DHL.mean <- mean(DHL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DHL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DHL.mean) #add mean to plot
###########################################################################

mean(DHM, na.rm = TRUE) #remove NAs from dataset, then find mean 
DHM.mean <- mean(DHM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DHM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DHM.mean) #add mean to plot
###########################################################################

mean(DHH, na.rm = TRUE) #remove NAs from dataset, then find mean 
DHH.mean <- mean(DHH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DHH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DHH.mean) #add mean to plot
###########################################################################

mean(DHG, na.rm = TRUE) #remove NAs from dataset, then find mean 
DHG.mean <- mean(DHG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DHG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DHG.mean) #add mean to plot
###########################################################################

#########################################################################
      ## Lights On Extreme Flow

############################################################################
mean(DEN, na.rm = TRUE) #remove NAs from dataset, then find mean 
DEN.mean <- mean(DEN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DEN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DEN.mean) #add mean to plot
###########################################################################

mean(DEL, na.rm = TRUE) #remove NAs from dataset, then find mean 
DEL.mean <- mean(DEL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DEL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DEL.mean) #add mean to plot
###########################################################################

mean(DEM, na.rm = TRUE) #remove NAs from dataset, then find mean 
DEM.mean <- mean(DEM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DEM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DEM.mean) #add mean to plot
###########################################################################

mean(DEH, na.rm = TRUE) #remove NAs from dataset, then find mean 
DEH.mean <- mean(DEH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DEH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DEH.mean) #add mean to plot
###########################################################################

mean(DEG, na.rm = TRUE) #remove NAs from dataset, then find mean 
DEG.mean <- mean(DEG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DEG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)  ### larger stack number = more zoomed out
arrows.circular(DEG.mean) #add mean to plot
###########################################################################

<!-- rnb-source-end -->

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<!-- rnb-text-begin -->



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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuc3RyKENDLlRvdGFsRGF0YSlcblxubGV2ZWxzKENDLlRvdGFsRGF0YSREX1YpXG5gYGBcbmBgYCJ9 -->

```r
```r
str(CC.TotalData)

levels(CC.TotalData$D_V)

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<!-- rnb-text-begin -->


Tests against each other


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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxud2F0c29uLndoZWVsZXIudGVzdChkYXRhMSlcblxuYGBgXG5gYGAifQ== -->

```r
```r
watson.wheeler.test(data1)

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<!-- rnb-output-begin eyJkYXRhIjoiV2FybmluZyBpbiB3YXRzb24ud2hlZWxlci50ZXN0LmRlZmF1bHQoeCwgZ3JvdXApIDpcbiAgVGhlcmUgYXJlIDEyOTc5MCB0aWVzIGluIHRoZSBkYXRhLlxuICBUaWVzIHdpbGwgYmUgYnJva2VuIGFwcGFydCByYW5kb21seSBhbmQgbWF5IGluZmx1ZW5jZSB0aGUgcmVzdWx0LlxuICBSZS1ydW4gdGhlIHRlc3Qgc2V2ZXJhbCB0aW1lcyB0byBjaGVjayB0aGUgaW5mbHVlbmNlIG9mIHRpZXMuXG5cblx0V2F0c29uLVdoZWVsZXIgdGVzdCBmb3IgaG9tb2dlbmVpdHkgb2YgYW5nbGVzXG5cbmRhdGE6ICBleHAgYW5kIGNvbnRyb2xcblcgPSA3OTIuNywgZGYgPSAyLCBwLXZhbHVlIDwgMi4yZS0xNlxuIn0= -->

Warning in watson.wheeler.test.default(x, group) : There are 129790 ties in the data. Ties will be broken appart randomly and may influence the result. Re-run the test several times to check the influence of ties.

Watson-Wheeler test for homogeneity of angles

data: exp and control W = 792.7, df = 2, p-value < 2.2e-16

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<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Saving Data.frames as csv files

<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuc3RyKGRhdGEpXG5cbmBgYFxuYGBgIn0= -->

```r
```r
str(data)

<!-- rnb-source-end -->

<!-- rnb-output-begin 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-->

‘data.frame’: 2765343 obs. of 48 variables: $ Date : chr \20191118 \20191118 \20191118 \20191118 … $ File.name : chr \20191118_view1_ \20191118_view1_ \20191118_view1_ \20191118_view1_ … $ X : num 0.319 0.323 0.162 0.163 0.173 … $ Y : num 0.2899 0.3322 0.0433 0.0405 0.0371 … $ Z : num -0.0684 -0.0662 -0.0774 -0.0774 -0.083 … $ Track : int 1 1 1 1 1 1 1 1 1 1 … $ View : chr \1_ \1_ \1_ \1_ … $ D_V_T : Factor w/ 342 levels \20191118_1_1,..: 1 1 1 1 1 1 1 1 1 1 … $ D_V : Factor w/ 78 levels \20191118_1,..: 1 1 1 1 1 1 1 1 1 1 … $ Flow.rate : Factor w/ 5 levels Flow,Flow,..: 1 1 1 1 1 1 1 1 1 1 … $ Chlorophyll : Factor w/ 4 levels Chlorophyll,..: 1 1 1 1 1 1 1 1 1 1 … $ Guano : Factor w/ 2 levels ,: 1 1 1 1 1 1 1 1 1 1 … $ Light : Factor w/ 2 levels ,: 2 2 2 2 2 2 2 2 2 2 … $ dx : num -0.00058 -0.00116 0.00141 0.00283 0.00705 … $ dy : num 0.01228 0.01437 -0.00273 -0.00183 0.00015 … $ dz : num -0.000559 0 0 0.00056 -0.000559 … $ d : num 0.01231 0.01442 0.00307 0.00342 0.00707 … $ vx : num -0.0174 -0.0348 0.0423 0.0849 0.2115 … $ vy : num 0.3684 0.4311 -0.0819 -0.0548 0.0045 … $ vz : num -0.0168 0 0 0.0168 -0.0168 … $ v : num 0.3692 0.4325 0.0922 0.1025 0.2122 … $ heading : num -0.0472 -0.0805 2.6647 2.1443 1.5495 … $ pitch : num -0.0454 0 0 0.1647 -0.0791 … $ vel.turn.angle : num 0 3.23 157.29 31.14 36.72 … $ vel.flow : num 0.3684 0.4311 -0.0819 -0.0548 0.0045 … $ trim.X : num 0.319 0.323 0.162 0.163 0.173 … $ xsmooth : num 0.319 0.319 0.173 0.18 0.319 … ..- attr(, = int 31 $ ysmooth : num 0.2899 0.2899 0.0433 0.0433 0.0717 … ..- attr(, = int 31 $ zsmooth : num -0.0684 -0.0684 -0.0774 -0.0774 -0.0684 … ..- attr(, = int 31 $ smooth.dx : num 0 -0.14574 0.00705 0.13869 0 … $ smooth.dy : num 0 -0.24663 0 0.02842 -0.00753 … $ smooth.dz : num 0 -0.00895 0 0.00895 0.00224 … $ smooth.d : num 0 0.28661 0.00705 0.14186 0.00786 … $ smooth.vx : num 0 -4.372 0.212 4.161 0 … $ smooth.vy : num 0 -7.399 0 0.853 -0.226 … $ smooth.vz : num 0 -0.2684 0 0.2684 0.0671 … $ smooth.v : num 0 8.598 0.212 4.256 0.236 … $ smooth.heading : num 0 -2.61 1.57 1.37 3.14 … $ heading.pi : num 0 -149.4 90 78.4 180 … $ smooth.pitch : num 0 -0.0312 0 0.0631 0.2887 … $ pitch.perfect : num 0 -1.79 0 3.62 16.54 … $ turn.anglexysmooth : num 0.833 0.833 1.326 1.335 1.35 … ..- attr(, = int 31 $ turn.angleyzsmooth : num 1.8 1.8 2.63 2.63 2.33 … ..- attr(, = int 31 $ turn.angle.smooth : num NA NA NA NA NA NA NA NA NA NA … $ vel.turn.angle.smooth: num 0 NaN 120.6 12.1 100 … $ turn.anglexy : num 0.833 0.772 1.309 1.327 1.359 … $ turn.angleyz : num 1.8 1.77 2.63 2.66 2.72 … $ turn.angle : num NA 3.23 157.29 31.14 36.72 … - attr(, .action)= ‘omit’ Named int [1:1273476] 1 2 3 4 5 6 7 8 9 10 … ..- attr(*, )= chr [1:1273476] \19 \31 \47 \56 … ```